{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "\n",
    "# 加入下述代码！可能！是因为torch包中包含了名为libiomp5md.dll的文件，\n",
    "# 与Anaconda环境中的同一个文件出现了某种冲突，所以需要删除一个。\n",
    "import os\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fri</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sat</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sun</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Mon</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Tues</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  week  temp_2  temp_1  average  actual  friend\n",
       "0  2016      1    1   Fri      45      45     45.6      45      29\n",
       "1  2016      1    2   Sat      44      45     45.7      44      61\n",
       "2  2016      1    3   Sun      45      44     45.8      41      56\n",
       "3  2016      1    4   Mon      44      41     45.9      40      53\n",
       "4  2016      1    5  Tues      41      40     46.0      44      41"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = pd.read_csv('data1.csv')\n",
    "\n",
    "# 查看数据（默认查看前5行）\n",
    "# https://blog.csdn.net/qq_40305043/article/details/104851766\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据表中\n",
    "* year,moth,day,week分别表示的具体的时间\n",
    "* temp_2：前天的最高温度值\n",
    "* temp_1：昨天的最高温度值\n",
    "* average：在历史中，每年这一天的平均最高温度值\n",
    "* actual：这就是我们的标签值了，当天的真实最高温度\n",
    "* friend：朋友预测值（意义不大）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据维度: (348, 9)\n"
     ]
    }
   ],
   "source": [
    "print('数据维度:', features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理时间数据\n",
    "import datetime\n",
    "\n",
    "# 分别得到年，月，日\n",
    "years = features['year']\n",
    "months = features['month']\n",
    "days = features['day']\n",
    "\n",
    "# datetime格式\n",
    "dates = [\n",
    "    str(int(year)) + '-' + str(int(month)) + '-' + str(int(day))\n",
    "    for year, month, day in zip(years, months, days)\n",
    "]\n",
    "\n",
    "# datetime模块的datetime对象提供的strptime方法可以将字符串转为datetime对象，转换时要求字符串内容符合指定的格式。\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2016, 1, 1, 0, 0),\n",
       " datetime.datetime(2016, 1, 2, 0, 0),\n",
       " datetime.datetime(2016, 1, 3, 0, 0),\n",
       " datetime.datetime(2016, 1, 4, 0, 0),\n",
       " datetime.datetime(2016, 1, 5, 0, 0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示前5行的数据\n",
    "dates[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'module'>\n"
     ]
    }
   ],
   "source": [
    "print(type(datetime))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fKy0tDf3798f//vc//PHHH7jmmmtcYfzRo0cr7u8874ULF5jvO6ve3Y0ep7ErNTygtLTU6zWDwYAJEya4OoXs2rUL69evxxdffIHhw4dj7969PnkfH374YXz55Zd4+OGHIQiCYjRt+fLlyMnJwT/+8Q9Mnz7d4729e/dKeioB4MUXX0R2djaSk5Oxd+9eLFy4ULa4VC2+XgOBQOp6cEb0WEZwuEE5poTPJCYmYvjw4cjNzUVMTAzuvfde2e2d7ZYGDRrk1f903759MJvNzP1Onz6Np59+Gk2aNMGWLVswYMAAfP3111i8eLE2H+QSzqT6YCseAK7iDHeysrIgCAIzLEQQRGCIj49H586dkZ2drboYyGmAfvbZZzh16hR2796NPn36eOSjS9GhQwfExsbi0KFDzPNu27YNgCOtyokzL/3cuXNe25eWluLEiROy52zWrBkGDx6Mjz/+GPfccw8KCwuxe/duRVlZ3HzzzcjIyEBubi569+7tlf4kxvl8YIX7WfrQyfr167Fw4UL07NkTWVlZaNWqFWbOnOl3ioM7/lwDWnPu3Dnm9Cnnd9QQng9kmBJ+MWPGDKxYsQJr165VnIjhzJXZsWOHx+sXL17E888/z9zHYrHg0UcfRWlpKT766CO0adMGCxYswGWXXYZ//vOfmubTTJgwAZGRkXjxxReZo/JsNhu2b9+u2fnkeOuttzxCkGazGXPmzAGgXDRBEIS2PPXUU7BYLHjyySe9+i0DQHl5ObPX8qBBg9C0aVOsWbPGFeF54IEHuM4ZERGB+++/H1VVVXj55ZchCHWzcM6fP4/33nsPOp0ODz74oOv1Dh06IDExERs3bvToI2q1WjF9+nSvxX9NTQ1++uknr56ZgiC4PHDR0dFc8orR6XRYtmyZK0VACefzQaxj9+/fj/fee4+5T05ODiZNmoTExER88sknSE1NxeLFi2G1WjF+/HjJ9k2+4Os1oDU2mw2zZ8/2+M1OnTqFxYsXIyIiQtFBFA5QKJ/wi1atWqFVq1Zc2zpzf9avX48BAwbg+uuvx4ULF7B582ZkZmaiZcuWXvvMmjUL+/btw+OPP+5aSScnJ2Px4sUYOnQoxo8fj59++km2EwAvmZmZmD9/Pp566in06dMHt99+O9q3bw+bzYbc3Fzs2bMHNTU1OHPmjN/nUqJjx47o06cP7rrrLhiNRmzYsAGnT5/GoEGDqFUUQdQzY8aMwf79+7Fw4UJcc801uO2225Ceno7S0lKcOXMGWVlZuPXWW716d0ZFReGee+7B4sWLMW/ePK7epe7MmjULu3btwrJly3DgwAHccsstKC0txVdffYWSkhJMmzYNvXr1cm0fERGBp59+Gq+++ipuvvlmDB06FIDD2BMEAV26dPHoBGI2mzFs2DC0bt0a1157LS6//HJYLBbs2LEDBw8eRK9evXDzzTf7/L1169aN24M3atQofPjhh5gxYwZ27NiB9u3b48SJE9i0aROGDh3q1UDe3WmxdOlSV0FQnz59MGPGDLzyyiuuFoRa4Os1oDVXXXUV9u3bh1tuuQX9+/dHSUmJa9Trq6++ijZt2gT0/PUBeUyJesNgMOCzzz7Do48+ivPnz+M///kPdu/ejbFjx2Lt2rVe7Uq+++47zJ8/H127dnV5C5307dsX06dPx/HjxzFlyhTNZBw5ciS2bduG0aNH4/Dhw1i8eDE+++wzHDt2DLfffjsWLlyo2bnkWLJkCUaPHo2NGzdi0aJFEAQB06dPx3//+19NW6AQBMHHm2++iTVr1qBv377YsWMH5s2bh/Xr1+PixYt49NFHmW2mgLoIh8ViwZAhQxR7l7qTlJSETZs2YcqUKaioqMD8+fOxZs0adO7cGcuWLcOMGTO89nn++ecxZ84cxMfHY+nSpVi/fj1uuOEGbNq0ySuqFRcXh3/961+48sor8csvv2DhwoX4/PPPYTAYMGfOHHz99deSU5q0pmXLlvjuu+8wYMAA7N69G4sWLcLZs2fxzjvvYNasWV7bz549G7/++iseffRRr16xU6ZMQf/+/bFmzRq/22m54+s1oCXOa6JTp05Yvnw5Pv/8c7Rt2xaffPKJpn1bg4nOZDIJypsRBFEfdO3aFWfPnqUZzQRBEISLnJwcXH311bjhhhuwYcOGYIsTUMhjShAEQRAEQYQEZJgSBEEQBEEQIQEZpgRBEARBEERIQDmmBEEQBEEQREhAHlOCIAiCIAgiJCDDlCAIgiAIgggJyDAlCIIgCIIgQoJGbZhmZ2cHWwRFwkFGIDzkDAcZnYSLrOEgZzjI2JAJh+8/HGQEwkPOcJDRSbjIGg5yailjozZMCYIgCIIgiNCBDFOCIAiCIAgiJCDDlCAIgiAIgggJyDAlCIIgCIIgQgIyTAmCIAiCIIiQgAxTgiAIgiAIIiQgw5QIaUw1dqw9WYUDRbXBFoUgCKLBcrjEgjUnq1BUbQu2KEQjxxhsAQhCihqbgFvWX8DpchsMOuCz25Ix4PLoYItFEATRoNhVUIO7vy9ErR1Ii9Fj74hUJEaS34oIDnTlESHL6hNVOF3uWL3bBGDCz8VBloggCKLh8VyWCbV2x7/zzXZ8fKgiuAIRjRoyTImQZc8Fz/C9qVYIkiQEQRANl0Mmq8ffW/NqgiQJQZBhSoQwBl2wJSAIgmh82OzBloBozJBhSoQsBh1ZpgRBEPWNTaDoFBE8yDAlQhbymBIEQdQ/NrJLiSASVMO0vLwcL7zwArp06YK0tDQMGDAAv/32m+t9QRDw+uuvo1OnTkhLS8PgwYNx+PDhIEpM1CcGWjYRhN+QniXUQoYpEUyC+uj/+9//ji1btmDBggXIysrCrbfeimHDhiEvLw8A8MEHH2DevHmYO3cutmzZgpSUFAwfPhzl5eXBFJuoJxpCKL+42oayWkrYIoIH6VlCLTZ7eFmmdkFAfpUNNWRRNwiCZpiazWZ88803mDVrFm666Sa0a9cO06dPR9u2bfHpp59CEAQsWLAAkydPxt13343OnTtjwYIFqKiowJo1a4IlNlGPGMPcLn3/QDnaf5aPTl/kY32OOdjiEI0Q0rOEL4STfVdjE3D394Xo9EU+bvz6As5VWJV3IkKaoBmmVqsVNpsN0dGeDdNjYmKwa9cu5OTkoKCgAP379/d4r2/fvtizZ099i0sEgXAO5ZfV2jF7XxkEAFVWAdP3lAZbJKIRQnqW8IVwMkzXnqzC9nxHa8HsUisWHq4MskSEvwRt8lNCQgKuu+46vP3227jyyiuRmpqKNWvWYO/evWjXrh0KCgoAACkpKR77paSk4Pz585LHzc7OViWH2u2DQTjICGgvp6kkAkCEpueor+/yz3I9gDpj4FylrUFem0B4yMkrY2ZmZoAlqV9Iz/ITDjICgZIz1uMvc22tX+epz+/yjX3RcPexffhnBR5KusC9f+P+3bVFKz0b1JGk//nPf/DUU0+hc+fOMBgMuPrqqzFy5Ejs37/ftY1OlGcoCILXa+6oebBkZ2eH/IMoHGQEAiNnSkUZcNYzz82fc9Tnd1mQXwPsL/R4raFdm0B4yBkOMgYS0rPKhIOMQADl3JHr8afeGIHMzMt9OlR9f5f23/MB2Dxe4z1/o//dNURLGYMaLG3bti02btyI3Nxc/PXXX9iyZQssFgvatGmD1NRUAMCFC54rn8LCQq/VPREYFh+uwOUr8tB9TT7+KKxV3kFjWO2ihDDpr0dJ+ESoQHo2tDFbBYzbWoS0Zbm4f3MRyi3BL5a0B18EbkjXNjxCIosvLi4OaWlpMJlM+PHHHzFo0CCX0ty6datru+rqauzatQu9e/cOorSNg7JaO6buLkW5RcCpchtm7yurdxlY6iYEdDYX1VZv6cPFqCYaJqRnQ5ONZ8z4+nQ1qm3AprPV+H8ngl8oaQ0jXVUTZh0ECGWCGsr/8ccfYbfbkZmZiVOnTmHmzJnIzMzEmDFjoNPpMHHiRLzzzjvIzMzEFVdcgbfffhtxcXEYOXJkMMVuFPx8vsbDMPwpCLOTrQwjtMYuIDIMOu9XMgxTmxD+nQaI8IP0bGgzOcvk8feUXSaM7xQXJGkchJMTkjymDY+gGqZlZWV4+eWXkZeXh6ZNm+Kuu+7Ciy++iIgIR8HLM888A7PZjKlTp8JkMqFnz55Yt24dEhISgil2oyAU7CfWWLxamyCuhwpJWOG4WrsAoz4UvlmiMUF6NrQJRY0QTiNJa2zK2xDhRVAN0+HDh2P48OGS7+t0OkyfPh3Tp0+vR6kIAAgF+8nG8piGgBL68GA5lmdXoXtyBN7qk4Qmkd4ZMeW1LKMaiA3qHUc0RkjPhjahOEckFJyQpho7puwy4a9iCx7uGIeJV8Vz7cdQx0SYQT8hwUQfAtqSEQ1HbZDziQ4WW/DSr2XILrVi9Ukzlhxh98yT8pgSBEG4EwpOADGhUPz08aEKrDtlxtFSK6bvLcXxUgvXftFhkOpFyEOGKcEkFJSllWHIBTufaM5vnkVgUkVhZUyPKRmmBEF4og/BYH4oqKo3/vBsFfjxIb7G+VFkmIY9ZJgSTELh1mYpx2AbpsXVfLkEZQyPabh0FCAIov4IBSeAmFDMMWU5KliQYRr+kGFKMOFVlja7gHf2l+OZv6KwIrtS05ZILEVUG2TjjlVtz6LcwpI99JQ9QRDBhTdr6oLZhtnHIjHmxyIcLOYLa/sKp5qrV2IjvL8oG0OnUo5p+EOlGAQTlq5kTYNZd8qMV34rA2BA1g4Trmoage7NIzWRgaUcg+0xreI1TBkWdLCNaoIgQg9eO2rqbhM2XDACqMb+IgsO3psqO53LH0JxDR1r8P6mKhj6OBSNakIdtLYgmLBsKNYN//jPJR5/v/hLqWYyMD2mQTZMzZxaj2XAWkIhcYsgiJCCNzr19elq17/PVdoC7jUNNWIYTaCZejYUrWpCFWSYEkxYze15jMILZu3cgswc0yArHV6PKcs7SqF8giDE+NoBRcuc9VAb/MFKCYtlCMl6JtWGQEtBwj/IMCWYsJLfeRShlhcUu/hJwxP4AK9hylq1UyifIAit0LJoimUbs/I36wuWnjcyHi4sPUse0/CHDFOCCctjyjM/WcuUp1BsFyU+vZSngbWSJ4VJEIQYXw1MLZ2coRadYvWBZslIkamGCRmmBBPWvc0TItFSWbKck0XVoeV2ZFWKAhIKk3JMCYIQEex2UXZBYOr7wiDqWlYfaN6aA4pMhT9kmBJMWN5RHo+fpqt4xvmOmkIr4T9OwmVKoXyCIHjw9SGsVXRKar18zGTV5gQ+wPKYsh4/rPQyuxDcNATCf8gwJZiwlBUrvC9G01A+Q4ajpfzKsqDKho8OlmNDjtlvWSotdvznUIXX6zESzZxZ4STymBIEIYan+MnOcBRopWql9PphFU6A3QU1+PBgOY5o4Dg4ZrLg1d+8J+qpGVFNToDwhvqYEkzYze15cky1s0xZCvNwiYXZT1VMrU3AresvIK/KcZD3+iThRoPvstz7vyJkFdR6vc5qYeI4P+M1WsUTBCGCJ5TPWtNqpU6kageOcnpMs/JrMPi7QggAXvu9DKu765Dpoyy5lTb0++YizIwPzPKCSkXxau0CYkJifiHhC+QxJZiwFCFPKF/LC4qlME21ArOpspivTptdRikAPLvL5LMcOeVWplEqB8sIpZGkBEGI4TGfWLpXqwCMTUIvnanga4EyZZcJTlGqbcDCMxE+y/L2/jKmUQpIeEwlRKRC0/CGDFOCCUtZ1XcoX0q3SClSd46pCPkrUVwjfUKWjIIgMI1QCuUTBCGGx2PK0r1azbOXOg7v8Y+IPKu/l/luVmTlSzsA2FX5Eh5T6mUa1pBhSjBh9zGt3+InVjoBwKcwtZRD7nSsVbyUZ1TOwCUIonHCs5hn6UKt1rlSDgdfnY7+2Mtyu6oJ5Qe7rSDhH2SYEkzYiebK+wW6+AngU5iaem5l3mMpS6lV/Bt/lEsa2wRBNE54ip9YupAngsWDP3pWa+TOydvHFABmajgam6h/yDAlmLCr8us5x9SPlbyWHlM5T7EajykAbD9fo4FEBEE0FHh0JrstkjaWo5Re9/X4/kglyOzNqjmQ8ox+e6Ya+VUUzw9XyDAlmLDDJsr7aemplOpFxxOl0dIwlQsLsQcRSG//W0VpwRAAACAASURBVGFo9WElCCK4BDuUL3UcX4/vl2Eq5zFlPH/knAafH6/yQxIimJBhSjBR0zPOHb2GJqF0iKl+R6OaZboAqG2rlRxNtxxBEHWwVJUg0nHs4idtzi/tMfXteP55TKVhhvJlnKIrsqu8vkciPKCnJMGE5a2s9wb7UgpTu1NwUSOj/NSG8qs4Wl0RBNF4YGkEsRHGCmNrVZWvdY6pP2LJnZP1PJDzmB4vs2KXyjZ/RGhAhinBhG1wcXhMQ6T4ScsLW6qvHsB+OMh5TMtoJAlBEG6wDS75vwG+tnm+nh/QzvBVg2qPqcJ3sDybwvnhCBmmBBM1PeMChdTp+KryvS1kX/WsXI4p6+Egl2NabiGPKUEQdTDz1EUvBjLHVEqf+qov/RFL1mPqwzPp69NmcgaEIWSYEkx8DuUDMNXYUeW2cWmtHRU+jD3yp1qUta/aKLrVLqCgyoZKmR3ZE7Kkj1lO458IgnCD3QpKUNzGYhdQVG1zbSsIjr/VDvKQjEypOkodvhimVVY7iqrlq+iZvbUVPmuVVcC6U2YfJCKCiTHYAhChia8jSbMKapGx6jyaROqw7NZmOFZqxfQ9pTDqgfk3NsWIdrHcMkgpTB69y5JVzcK5uNqG4T8UYX+RfBW9mhYmAFBeSx5TgiDq4OmAwlpoP7qtBABwdXIEVt+ejL9nmbDpbDXaJxqwdkBzZCTwPd6lQ/lcu3shCOryubbl1eChrUUor5VrFiURnRK99sq1iSirFfDW/nLXa8uPVeLhjnGqZCKCC3lMCSYsg0uNs6+0VsCzWSZM3V0Kq+CYofz0TnXz6v2pFmUZoWpswrWnzIpGKaC+hQl5TAmCcIcnn19ObewvsuD+zUXYdLYaAHCizIb3D5RL78BxfqD++pi+e6AcZQpGKcCXXhap12FMpqfzY1+hBYdKqE1fOEGGKcFErcHF4mS5Z2hGLiTOlMEPjykrnFVr51/Jv/kHn2JXOyGLckwJgnCHpVbFhqhSIdIfokX0f4/xF/340y+ahdrdtnEOHWEWidm8DdOMBCP6tYzyeH35sUqVUhHBhAxTgona3MlAID35SVn1sWRVI39CBL8RK5ZHtviJEvEJgnCDpw2SVuNHmefXuF1UoOCpyo8wOP7/UAdPr+kXJ8yyKVZEaEGGKcGEtUKf+0dZvcrASicAeEP5DI+pCr2UzpmfBXg/NOQM4DLymBIE4QbLXtqR7+lFVButUoM/o59ZBKrLFLP4iRHKB4Ah6TFIiqxzLhTX2LHxDBVBhQtkmBJMWMqq1g6P+cOBnKohCIJfCpNlmKrxmCaq8JiKHyxyLUwuVtsUq08Jgmg8sBbgL/9aJtomgOf3o/sJCzV7qXmG8OaYAkC0UYd723t6TZeqSG8ggkvQDFObzYY5c+agW7duSE1NRbdu3TBnzhxYrVbXNoIg4PXXX0enTp2QlpaGwYMH4/Dhw8ESuVEhFfU4U1H3+wRSWeZW2iQVHI99aWHYfmpyTOXGkIoRP1jkDNMaG/Da7/yFCQThD6RnQx+Wro0yeOoqKeNRC85WshfK9RHKV5PZxHSWiESPcLNoxnbwrMT/Ka8Gx0upCCocCJph+v7772Px4sWYO3cu9u7dizfeeAOLFi3Cu+++69rmgw8+wLx58zB37lxs2bIFKSkpGD58OMrL6cEeaKTC6O5eR1/CS7yr8CMmq+R7PMfwN5SvplBLXCjGMordWXK0EgeLSUESgYf0bOjDU2gayBzToya2LuI5JcvjqeaxoGZEM1co382g79osAtemRHi8/+lRKoIKB4JmmO7duxd33nknBg4ciDZt2mDQoEEYOHAg9u3bB8BxwS9YsACTJ0/G3Xffjc6dO2PBggWoqKjAmjVrgiV2o0Fq3J37yt0XZcmrtI5IKEvA9z6mPKF8s1XA87tMyFIxY3l5diX6flWAMT8WoaDK5mUUj+sQi3YJBtffdgGYvscU0FQIggBIz4YDLCeAd9564HSFlBOAZ+Qps8iUU9QTpVYM2HCRb2M4nAVTskzova4Ar/5WBkEQvDyukSKL5rEr4z3+Xpld5TH8hQhNgmaYXn/99dixYweOHTsGADhy5Ai2b9+O//u//wMA5OTkoKCgAP3793ftExMTg759+2LPnj1BkbkxIWX8uSsiX8JLvIWRR2U9psr7M/uYcoTy15yswuIj6lbVM38pw6ESKzacqcbcP8q9DNP4CD1e693E47Ud+bX4Jqda1XkIQi2kZ0MfpsdUZKwGMm1Kyglg58gW9SeXf8YvpcguldbzYn4vtODTo5U4WmrFW/vLsTWvRtZjCgB3t4lBclSdmVNaK2DtSSqCCnWCNvlp8uTJqKioQO/evWEwGGC1WvH888/jscceAwAUFBQAAFJSUjz2S0lJwfnz5yWPm52drUoOtdsHg2DIaCqPBOvyyDmXh2yzI1ZdVAsA/JOcAOBY9nFEG5S3O1PEPj8AnD17Dtnl8tqvtCIKgOeJLILyd/n0TnWfR8ynRyvxt/RaAJGu1ypLS9AuyYLrk6Kw21Qn0wtZhWhXXS35fYTDtQmEh5y8MmZmZgZYkvqF9Cw/wZLRKnjrHItN8JAnL98Id53CA8/nEQSgpIat86xWu+IxHCmbnvvboMORY9kwKPgBNp31T9c+ue0imkYIcNfzBbnnkF3m+WwYnBKBZefqQvrz/ihGb+RBd0m+cLg2gfCQUys9GzTDdN26dfj888+xePFidOrUCQcPHsQLL7yA9PR0jB071rWdTud5dQuC4PWaO2oeLNnZ2SH/IAqWjDE5RUCRt0cvJa0lMjNiAACxlTZgb76q47Zt3x7xEcqO+qjThUAxu/Fyy1atkHlZtOz+hqMXAHh6AmrtHNfHjlxF2ZTQJzQFUOd1bd0iGR06JOCDFhbc8NUFl/fjfI0e31WnYto1iV7HCIdrEwgPOcNBxkBBepaPYMloFwRgR57369Ch/RVXQH/pN2hmqQBOlKo6Ns/nqbUJwE7v8wMA9HrFY+RX2YA93s+A9HbtEWtU0PN+6tr8Gj0So40A6ryundqlIzPJM690SpoVy9cUuPy/Ryr1KG/aBj1TIsPi2gQa3z0UtFD+Sy+9hEmTJuGee+7BVVddhVGjRuGpp57Ce++9BwBITU0FAFy4cMFjv8LCQq/VPaE9UpNGPHNMfSl+kn+/ymrHzF9K8cM56WkgXKF8RgFSfQ0IOCiawhJjdDxcOiZF4PErPStF3ztQgXMV/OEsANh7oQajfyzC1F0mlFHDfkIG0rOhjVy6o0ehaQBC+SdKrRi7tVjyfV/b8gFs/RsIxMVTMQw3bUaCEQNae06C4knXsgsCFh6qwP2bi7D4cAXVBNQjQTNMq6qqYDB4xjANBgPsdsfd2KZNG6SmpmLr1q2u96urq7Fr1y707t27XmVtjEjmmLq9Hojip/cPVuCjPyvkj8FxHlaxQK3A3y7KH34v9DRM44x15/3HNYloHl1325ltAt47KP953am2Chj5vyJsPFONRUcq8cq++h16QIQXpGdDG7mce3cdJjU21B8e2lqE789K57nznFKqA0l9TVkqrvF8GsRJ9J9+tJNnEdS6U1UoV/BU/Jhbg2l7SrHpbDWe312KnzlHpxL+EzTD9M4778T777+PTZs2IScnB+vXr8e8efMwZMgQAI7Q0sSJE/H+++/jm2++waFDh/Dkk08iLi4OI0eODJbYjQYpo9NdWUq1lJJDqdUTz4x6nmpRZruoenIuVohX8W6GaVKUHjN7eIbut+byF0GtzzGjzK3v1SKVhVpE44L0bGgjp0PddbDaaI+4Ol1McbUNh0rkIzVSUTN3pDym1fVkmPJ4TAHg9tZRaB1Xt0CrsQHHFQqvnt5R4vH3C3vUpVIQvhO0HNM333wTr776Kp577jkUFhYiNTUV48aNw7Rp01zbPPPMMzCbzZg6dSpMJhN69uyJdevWISEhIVhiNxqkQ/nsf/Mf10eB3OCrFvV+rSZIUe9Yo6eyHNkuBs/uMrk8EifLbTDV2JEUpbxOLAnWhyDCEtKzoY3cItsfJ4BzApIUPFrEn1C+ksc0UGHxGCP7c+t1OlzRxIhzbsMECqvtiGdu7SDf7Pktna2giX31RdAM04SEBLzxxht44403JLfR6XSYPn06pk+fXo+SEYBcuyiB+W9etIhIcfXXY3yAGhWTn+Rom2DAqXJ+JRUnUpZxEXp0SjJ6eCx+L6zFra3kC7qAwOSaEQ0X0rOhjZxX0uKHEyBCofMJjweWr180+3Wzws487a8SI3Ue0SElYo06V7EYC/cUKsBhmGZwHx1QquUitIO+aoKJlPHnroh88X5q4zFVht3H1P9zR+iBRKU4mQjWKr5Hc8/WL78V8k2CYhncBEGEJ7w5pmoLTZU8prUcekSAsmdT6jhKHlMep0bHJur8ZlJhfCfehqk6D2iEwndKaAcZpgQTqdCR1W+Pqf+GFVdSPmMjLaLgMQadYn8+MeJQPsAyTPkmTQVyAgxBEPWLnCfUI21K5W2vZETxemCVTiulj8wKjUZ4qvbjONoKuhMrUfjkpLmoYXShWd0DQaU4hB/QV00w4Zr8FCyPqY9J+VqE8qMMOhhlwkUsxKF8AOjR3LPX3u+8hinZpQTRYJAN5Qu+OwGUgjpSuaFilPS1VBRKyWPKkzPL0pv+bO/lMVXpqTCSx7TeCFqOKREa/JhbjUPFFoxoF4tWblWLUqEjf8JLgPIKnAel0wqCwFyRS+khi13AZ8ersJ2jHUi0UQeDyuVcDCM5qXPTCETq6xR7XpUd+VU2pMWyk8NsdgFfnKjCfw4pt5aqtQn4/EQV7ALwwBWxiFLr4iUIQlNqbQ4dAwCj3O5J+VB+3b958urdEY/m9D42nyZW2kwqlC+VY5pXacOak1X4TqZNlZN4BQ+oGKnCJyfJjBxTFoXVNqw+4T22lJUekVdpw9qTVbiyaQRub61cI0DwQYZpI2bdySqM3+ZoifHewQrsvzcVCZfiFVIKye+qfA3C6UrK0iawDWCp1f2T20vw/zjnJ0f7EMpnreQjDTp0S47Arxfrckt/K6zFoPQY5jFe2FPK3Rpq0s4Sl2LdkluNZf2T1QlMEISmTNxegrWnHPfktvM1+PSWZgDk9aE/aVNKKoq3/ZTSaaWOw/KYVlrsuOnrCyji9FTyTAh0h5Uy5Y7YY1rEyDG12AXc8s1Fj+p9J2JxKi129PvmAi5eMnAX3twU97X3b8wq4YBC+Y2Yd90auxfX2PFvt8b2UmF6v6vyNfCZKoeXJBLyGaF8uyBwG6WAwzBVG9KRWsl3V1EAxWuUVlsFj9X+NznVPnm2CYLQBkEQXEYpAKw7ZUblJYtOLqTt2S5K3TkVPZ3coXyF4icVfUz/30kzt1EKqA/lxyp4DFJEhulFRo7pV6fMTKMU8A7l/+dwpcsoBYB39iv34Cb4IMO0EfNnsachtPFMXXhFyuh0z3sK1RxTqcR6lk6sUJm0GW2AKo+pQSed7yUugJLKM1X6vO6Vs8dKvY1b3ocQQRDaw/IqZl9q7i7nufQYSaryHlbSs1p5TCUNU8bDYX8RXx69E6kpTlKoLX4qYoTyxc9Ed8Qe0+/OeDo0jio07Cf4IcOUcHGirO7Gkp78BLdtgtPHlCWaIAj4Mbca28/XSCrLX0sN+Oa0GRUWO/53rhq7CmpQrtow1UHNQj7OqINOolhKXAD1W2Etsz1LpcIKwP3toyZv5ah2agxBENrBMioPX7pP5XSo+3tqF/RKi1l/ckxNNXaszzHjeKlFciTpgkMV2FVQg6JqG9bnmHG63KrYwkqMao+pQqPRJpGeurvCKng5K2pkvhex/MfLyBANFJRj2oiJM+o8jB738W5SistdWQZt8hPjGM9kmbDsmKO44IErpPN8xm4t9vh7VHt2TqcU0Qb5Js5i5BLyM5sYkRChcxnHJTUCTpfb0DbR87YsV2gybbELrvYwLMOUQvkEETxY68qjJodnLlgeU54+poC3gVthsePGry/gXKUNUQZg0OVs/Xmq3IaBGwtdf8cadbiuRSRzWym0zjHV6XRIjtajwC2EX2Lx3Eeum4DY7i2p8dyWiva1gzymjZh2id7rEqfHTkph+qMsAfmVPG+PU/Fpq6x2l1EKwFX9ysPnjOpLOaKNOlUTQOSUpV6nw9XJ3l5TMeUKLk/3t0+WexumWgwWIAjCN1gLw5NlzlA+Z46pyntYaXPuUL7o71XZVa4czBob8OVpPv1ZZRXwU55y1xN3Yow6xSIud5QMU8C7Mr9EFLlX2XPfg7YJCuO2CG5UGaZlZWV4++23cdddd6Fv37749ddfAQDFxcX44IMPcOLEiYAISQSGKMZ95FwwcrWL8sERJ2fL+pr3VOWLID4SbdCpCjEpKUueCVBK6Qbuvwmr6ICa8ocXpGcbFiy95gwh83pM1UY97Aq61NdQ/o+5ym2etCLaoFPVMorHMBXnmYo9pqzcWCfuiwNWypVSKgHBD3coPy8vD4MGDUJubi7at2+PY8eOobLSUSncrFkzLFu2DHl5eZg7d27AhCW0hbUKt9odIQsphelvuyg5fehrpWh95lBGG3SIDqhhyvCYKrg8lR5gvvxORHAgPdvwYN2TtkuvyVXlW/1wAih1P+GNoojXuUr9UbUk2qDzSHVSgscwFVfmm8SGqUwo/5RbNIr1e1DKlHZwm/izZ89GWVkZtm3bhg0bNnitGAYPHoxt27ZpLiAROFirZqsrlB+YBvty7Ud4DSjxaZWmjGhJtEGHJipyn5RW0d1FBVAHiiyuh5aTMgXF/Ni2urxZlpFOVfnhA+nZhgfTiLn0mmwo3+0ttYtv5ap83zym9TmsI9oAJCqNsHLDl1B+cS1/jmmB2Y51Jx1pYqzvj4pMtYP7V9+8eTOeeOIJdO7cmVllnJGRgby8PE2FIwKLlMdUEASZPqZu/9Y4lM+bkC/ejHc/LYgy6JAQqU3xEwCkxxs8Gj9XWgWPlTmgnGO6M7/WlbPGWiyQwgwfSM82PJgOALt8Lr94P9WhfI0MU7EjQW1lvT9EXfKY8qKkawHvJvvFKjymADBppwkA+9lJDgDt4DZMq6qqkJqaKvu+XSmxhQgppDymcmEjjxYmPnlMpd/jvbHFRVIqRx77RWyEzjUdiwelfFSdTocMUdK8eFSeUlU+UNcjkEJM4Q3p2YYHy/h0Gjby7aLq/q127a3YGJ+zyMfbY6pODn+INepUVebz5P67j90GgPM1/B5ToK6egZ0yRXpWK7h/9fbt22Pfvn2S72/evBmdO3fWRCiifmAbMYELLwHyK3neUL5Y59anxzRW5SqeJ7yUJApXmUQJYEoeUwAwXPKusUNMpDDDBdKzDQ+mEaPQ/cTxnnL7PimUNuc1osS6tj5D+bFGnVdTe/ntlTfOSPAsq8mtVucxdcL63SgypR3cP/u4cePw+eef4/PPP3et2HU6HSorKzFz5kz8/PPPePTRRwMmKKE9Uqs+7vASZ3snd+RaQvEXP3n+XZ85prFGbUP5AJAUJTJMRf3xyjg8pk4Fzs4xVdydCBFIzzY8pFKmAKV2UfLHkEN5YhPfccSe1/rs1Rlr1GvWM9qJ2DDNq/bUvbwdXsgBEFi4q/InTJiAw4cPY+LEiUhISAAAjB8/HiaTCTabDU888QTuv//+gAlKaA8zxCTwh5e09pjyKkvxZvWZ2xNj1DaUD0h4TN1qong8ps45zhRiCm9IzzY82ClTl/4vc2t7DjPR1mPKnzYl2s+PPp9qiTWqn7KnRMtYPSL1dc+aUqsOphq7yznA4wQA2NFG8phqh6rJT++99x5GjRqFL7/8EidPnoTdbkfbtm0xYsQI9OnTJ1AyEgGC5fF0eEylb3B3Jat1jimv8vWuylcths/EGXWaV4qKDdNSkWHKmuksRpB50JHCDC9IzzYs5PK+uT2mGueYcuta0d/1HZ0yqnDR8uhavU6HNglGZLvNtc+psCIpKhJ2QUAJp3eEXWRKDgCt4DJMzWYznn/+eQwYMAB33303evfuHWi5iHqAGWISAqcsAa2q8kXFT/WpLCO0rxRtEuW5janGDsTV/V3EUd1VK/Ogq88cXMJ3SM82TOR6Cwcrx5Q7lK9ilrzWRBrUTdnj0bUAkBFv8DBMT5fbcHUyUFJjV/zeAEfXGqkcU0EQmN00CHVw/ewxMTH46quvUFpaGmh5iABRbRUwYVsx0lfk4aEtRai02CXamPCHl3xZIcqt5LlD+eLwUn2G8g16JKowTOM4NKt3KN/z84ir9Fk4fwu5nolEaEN6tmFwzGTBTV9fQMbKPMz/q0IiZUq6utu1jV8eU/n3eRerXmlT9RidAtTltPJO5BPnmeZcas/Ho2eBS+lujK9PgPruCQQb7vVIjx49cPDgwUDKQgSQ789WY/VJM8osAtbnVGPtKbNEGxOB22MqXk3zIF+V71son7eSUgviItS1MPGt+Mnzi71oVn4aOI16CjGFN6Rnw58395fjYLEFploBM38pRW6l9/3r8pjK3JqB7GPKa+iKi1XrU9cCQLwKlymvx7SNqD3f6XLH73OR0zCttQkyI7u5DkEowP2rv/HGG1i/fj0WLVqE2lrvsYlEaPPSr55emL/vNDFXd45QvvRx/AkvAUp9TPmOIVaWWq3ieXr0xRh0iDTocGUSX3q2v+2iLHbBy4PKwukBYVbl0zI+bCA9G/6sOWl2/dsmAJ8dr/LahivHVHA3TNXJIIA9z90Jt8c0iK35AODJq+KUN7oEj64FvD2mpy95THly+QGHjuWZjEj4Dnfx0/jx4yEIAv7xj3/gn//8J9LS0hATE+OxjU6nw+7duzUXkvAf3vvFahdgkbm//QkvAZCd4OxrpahSjmnzaD1XmKZZlB7nq+S3i7sUxn+3bxIe31aCcwxvCGt7OcQe01I3j2mxCmXp+L90BTAR+pCebXiUMHLE60aSSu/nMWXPB4PHLgBSbUd91rUK+3VKMuKIySq7jRquaBKBV3olYuavZYrb8raWkjJMC6v5PBy1Mi0VyTDVBm7DtHnz5khJSUFmZmYg5SECRDxnXqRU/owT91W8Tx5T2ZwqH1fxCvs93DEOVyYZ8ei2EtntmnIYps5VeZ/UKPx5X5rr9YyVeUzPZgxHQ+omor6o7h5T3rwn53fAstFJWYYPpGcbHqxKb6euk88xdeuA4sMtbBMAqSAQrwdWrcf045ua4tVd5/G/QlUNf2R5umsCnu7qaJ2WlV+DQd8V+nU8cSj/TIUNNrvAr2ttguT3R6F8beC+ejZs2BBIOYgAw5sYblPwmFr8VJZy9y1/02fPv5U8ppF6voklyVHKmS1SeUxSjZl5FJVc8RPvKt7iCuWzqvK5DkGEAKRnGx4sj6lTZcnpB6sGHlMp+IeZiDugyG8fadBBRTc91WgxeSohQo/kKL2r24lVAHKrbCg080enpIbLkBNAGwJ4CRGhBG/BjlKOqb/KUjbHlLtSVF2OaaRex5UY/0TneI+/M5t4r9tiJRRjt+QIr9eMOqBrM+W1X0KEziPkVmWtCxWVyVVHuFHrCuV7v0cN9gkieLDvSed7coWmvueYAsHpgBKpB6L1yvpmZo9EPgFEiGfdO7m5ZZSq42SIvKY55TaUcbo7a2UKhH35nQhvuD2mO3fu5Nruhhtu8FkYInDw5DoCDmUot6W/ylK+Kp/vGGp760UYdFwr7e7NI/Fs13i8f7ACbRMMeLpLPP6+0+SxjUGif8msnk0wbmsxit28Iy/2SESzaOWKKp1OhyaReo99yy6laVVzJojW2gUIgsAO5ZNdGjaQnm0cuNq7yTkBLt23giD41jNa5j3exara6FSEns9j+nDHWGzOrcaugloMbRON3y5akFulHNpJizXg+asT8MHBcpfBH2fU4Z/dE5RP6kZGghH7Ci2uv0+XW7k7DtTapZ039dm6sCHDbZgOGTKEq3FscXGxXwIRgYG/+AkKhqn7tupvwonbS3CyzIqpVycgUmQscifkX/p/hcWO134vw/y/KmW3j9Tz5XomROgwq1cTvNQzETqdDj+fr+GSBwBuahmF7FFpKKy2o1m0HgYdfzI+ACRF6lDsdrpyq2NfXmU5949y3CThNaiPSlq7IODjQ5XYmV+DoW1icH/7GGo07QOkZ8Mb3ml4VsHZqF3ZY+rr7fvA5iI80TkeQ9vEeL2ntir/iMmCN/8ox6lyeeMx0qADR0YUkqMN2DiwOQDHwvyqL/K55AEcC/4p3eJRY3PUThh1UK1rWB5TzqwpTNxegnEdYpnv1UeOaUmNHa/sK0NJjR1Tr0lA56be0bpwh9swXb9+vddrNpsNOTk5WLJkCXQ6HWbNmqWpcIR28E5HsgmQtWJtQt10C1+rvd/aX47ECJ0rod0Jr2HqjFC9/nu5olEKOEL5PB5TZ4GYU8mpNegMeh1SYzl6TjFIitIDbkq/TKVhCgBDJYoC6iO89G1ONWbsdbQk23CmGh2aGNEjJTLwJ25gkJ4Nb9Tcr3aBb8qer/fvjvxa7Mgvxm/3pKJdouejnteAEuCIxIzaXOTq9ylHpB6I5AjlA57GpNqJUrFGPWL9qK9qI67Mr7ByR6f+LLZg6m72EIz6SJv6xx4TVp9wtCTbe6EWB+9LVeUECQe4c0xvvPFGr//69euHsWPHYvPmzdDpdMjKyuI+cdeuXZGUlOT133333QfAYfy8/vrr6NSpE9LS0jB48GAcPnxY/SckAPArTKtduuLQiUUmn5EXVvsP7lD+Jct03l8VXNtHG3WKPe6MDA+nOG+0aVTgbn5xAVS5M5Sv4kEntWV9JORP3O7Z8cBppBLqID0b3qi5X5U6oLh6nSrMvVfitd+9dS2vTrAJwGGTlcsoBYBogw7RClbFqPbeHtwp3TydFE9dFe+1jZawWkZpMdq6PkL5TqMUcBRt/eaWktBQ0KT4yWAwYOTIkVi+fDn3Plu3bsXRo0dd/23btg06nQ7Dhg0DAHzwwQeYN28e5s6dVpmwIQAAIABJREFUiy1btiAlJQXDhw9HeXm5FiI3OngVplzz4LptlFud+ALrpm4Va/BSUmpP2zxarzjf/v0bkrxeaxFjwKRL54416jD/xqbqTqwCcS9Tl8dUgyak9aEsK0VynqnQrpch4YD0bOjDGw4GHHqUx2Pqy4Q9d/IYvZZZxU+3t4pC/8s804HsAr8OijHoEBehR5xRfnuxEQoAo6+IxdWXHAEdmhjxdJdAG6be05+0mGoVjHZRlQ2wiECzZmPV1dWq8p6aN2/u8ffy5cuRkJCAYcOGQRAELFiwAJMnT8bdd98NAFiwYAEyMzOxZs0aPPLII1qJ3WjgVS5WQVA0/PwNMSkd18mYzFi82bsJ1p4ye7z+/dlqTN/jWZQkR3KUHgkKXQkezGRPGJlzXRNMvCoeMQZwFTL5irfH1GGYKrVn4SEYlaJUAxAYSM+GNmoWklaZIhqgzgHgb8SDtbfYqfDZbc1wx+XReGBzkcfrHx4sly2icif5kqs0XkZNDsuIQYck75zIpCg9/jc4BXlVNqTFGBDN2d7QV1rFGmDU1XmsC6vtiNagFVUwOqAIsmNrwhO/PaZlZWXYsGEDPvroI3Tv3t2nYwiCgOXLl+P+++9HbGwscnJyUFBQgP79+7u2iYmJQd++fbFnzx5/RW6U8K4GbXblSUHOHm5Svdx8xSKSMTPRiLgIPcSF8AVmOxYcUs4tddI8Wo9oow4ROt/kbRVnCKhRCgBJojQBZ1W+WZNVfP0rLuqaoi2kZ8MDNV43myA9cx2oW1D6GzRhnUIcRbmiiRF6nc6riGh7fi125vONxm1+yTCNM0gLLBe5ijTokJFgDLhRCjjqAdJFFrTSFD8eguExbYhOAG6PadOmTSUr3wRBQHp6Ot5++22fhNi6dStycnLw0EMPAQAKCgoAACkpKR7bpaSk4Pz587LHys7OVnVutdsHAy1krKiJBs86JK/gwqV/SReuHDt+CiVRAirMfMeUQvy5LhRHAKhbTZcWFyI7Ox8XCwwA1PWpc8eUewpmPRBniIHUtLxgXwdCuRHu3/m5aj2ys7NRIPpOfKHQVIbsbP+mpcjh+O48q1QtVlvQv1N3eGUJ9sQl0rPBQwsZs8v1AKK5tj124iRKK6IgNZ+pvMqM7OxsnK/WAfDOy+Slymz2+mxVoudB7pkcIFqAuTISvgZSY+3VyM7ORrxR+plQXhZYXaSG5oYonJScjeUbp8/lIdscmIkmdb+hp649m5uLbIWJhfWFVnqW+wqcNm2al8LU6XRISkpCu3bt0L9/fxgMvv3IS5cuRY8ePdCtWzev47vjrAaXQ82DJTs7O+gPIiW0ktH6Sx7kJ9U7aNr80kPqhHTxSuuMDKTHG2H8swCA77mE4s8VX2gC8uo8oS1bpCAzMx6X6auAbPlxolIkROjQpaPjPHG/noXJyr5+gn0d3Bpfg/dO1SnsP8v1yMzMRFRBCYAqj20j9cCoK2Kx7FgVeIiNT0BmZjMtxXXhuj535Hq8rtcbgv6dOgmH+9wJ6dngoJWMF/JrgP18htflbdoi4kwJALZH0hAZjczMdBjLrMCvBT7LFBnlOI4Hv52He1yjQ7u2uCzOgMSzRUBxtU/nSW8Wj8zMdBz/47jkNklNEpGZGbhcfTVcdq4YMJllt2kdZ8BVzSKw6Szfd5KS1hKZGb4vIqTwuD5Fuvayy1ohszXfYiiQaHmfcxum06dP1+SEYi5evIiNGzd6eAFSU1MBABcuXEDr1q1drxcWFnqt7gk+qjntR6tdOWPFNbVE4xCCOLzk7HPqT75JsluJqMTQkJCgR/MI6HV1YZkcsx4lNXavStGxHWIxuWsCjpdauQ3ToISXGmDeU31Aeja8UV2VLztlT5scU5ZI4ml5zhR8ifkhXDS/lO4kp2dDqa2RUkHsF7cn4/rUSMz8hb/DSKBzTO2M9LmGONmP+5k/dOhQbNu2TfL9n3/+GUOHDlUtwMqVKxEVFYURI0a4XmvTpg1SU1OxdetW12vV1dXYtWsXevfurfocjR1BEPhzTBWUJRDIqnzPv7VRlu6GaejewPEReq9Gyb9erIVZlGB2W6totEs0IlKFkR2MaSQNUFfWC6Rnwxs1xU82hQ4ozoV/IHJMxeeN0DudAL4rW1eOqUxVvj+6XGsSIuWF+b/WUWgSqUekCqF5R736CsvJEOhzBgNuw3THjh24cOGC5PuFhYXc4/ScCIKAZcuWYcSIEUhIqGshodPpMHHiRLz//vv45ptvcOjQITz55JOIi4vDyJEjVZ2DcFy4vLrNytUuyvP/WiE2dJ3K0uDHKru5W9FSvEIbk2BzbYqnYbr3Qq2Xx9Q5wSpChbIsD0I7EY3r4hoNpGfDG3UeU6FeqvJZpxCf17nQNfgRnmoe49hZbsZICDlMZTu1ROrrvLsKDV08KAuwlci6FrTovxpqaNYuKjc3F3Fx7JY7Umzfvh0nT57EokWLvN575plnYDabMXXqVJhMJvTs2RPr1q3zUKwEH2qUpcXOnrfujtOAtGlsfXiF8vXOSUy+H7O928QTpcbPwebalEgsOVoXnv/1Yq3Xd+KcYKVmFX/MVP8NmBueqgwNSM+GNup0rXzUyRm58rePKSv8K9YrdR5T33HqWrmi+lBSwYmR0tK4t45S4xjJLg1s/2ZWNLPRGaYbNmzAxo0bXX//97//xU8//eS1nclkwrZt29CzZ09VJ7/55pthMrH7Uep0OkyfPj1gOVeNCVW99QTlec+B8pgGIu+pU1LdJV4qUfgUKlzXwrMTwta8Gq9tYi59HDWr+HyzHaYau0cT/z+LLcittOGWy6K4xrU6MdXYkVVQg85NI7ymp7hDoXx+SM82HFR5TO2CbJ6+Zn1MRbvbBW/ng9OY9McJ0InRn1RMSIXyZXJM3VtW5auosj8scgLYBQHb8moQF6HDdS3UdZYRBAE/n6+BUa9DyqXfi5WWFYxUrUAja5gePnwYa9euBeBQYL/88gv27dvnsY1Op0NsbCyuv/56vPHGG4GTlPAZVb317PLhJSBwOabSoXzfj+muLAtqQkgrMmifaETTKB1KaqS/V6cRqVbBHzVZ0DvVoRi/PFWFR7eVwC440gd+GJyiWIUNOIzSvl8VIK/KjjijDhsGNoeU764B6sqAQXq24aBm8pNNUMgxDVAfU7F+j9DXdWbwJ22qaZTyajmUQvmJMqt798X6yTJ+L+hRUT/Cv20vcY0Qnd0zEZMZU6+kmLq7FIuPOLrUPNI6Au91YDuDtBjCEmrIXknPP/888vPzkZ+fD0EQMG/ePNffzv/Onz+PEydOYM2aNbjiiivqS25CBWorRXlzTLWeKCROz3HmPfmzyu7g5jHt1YR9B6fGhEaASafT4doU6f6xQF2OaYzKJtTZbsr1kZ9KXA+rXy5asLOAr4H2wsMVyLvUL6/SKuBf+7xncDtpiNNIAgXp2YaDuslPAldVvr8OALH6l0qZAgLv0XRPrQo2SsVPTsSRLDkKqx3RKQC4YLZ5zLWfLaMvxVRY7C6jFACWnHM4WBpLjin3E7mkpAT33ntvIGUhAoS4slsOi13gnvxk8TPHVJz7JJ335Ju2vOPyaDRxyyMa2dLKVLzzbwqNvnoA0EvBMHWu5K9INHqkKShRaJZ+Ah4s4stB/VI0GnYLI9XACXlMfYP0bHijStdyekz9TZkS1wKIJ+y5Ow59NUw/uiHJ4++XeiZ6bdM0SofRV6jLjw4kcqH8sxV1ToxxHeJURe0uXnKb5/oxSUpqClVjMUxDZ/lCBIxKDVuYAI73BUHwyglVi8UORLlVcHqH8h3/V1MpGqkHXuyRCAHA+E6eSvCKOAEbBzbH/85VIylSjzKLgOtaROK2VsFvTuxEaXXu9JTqdDqsv7M5puwyYX2OcvNnce6TO7ytp9Q8IMkwJRojlSrCSEppU0497G8OoXIo373Qh/+416VEot9lUeiUZMSItp5N5Sd3jUdKtB5nKmyINepQZRUwJjNWdaQnkMhV5btzZdMIbBzYHHds5BuccK7ChswmEX4ZjAWMSU6CRBeHRpdjKmbr1q346KOP8Mcff6C0tBQCw2NWXFysmXCENqhZxSu1MAEcis0m+F95XWsXPHJ5vEL5PlSKNo3S4+9dpfN4rk+NwvWpvo83DTQ9mvN5TAEgJcaA2T2bcBmmX5wwY0xmDW5u6f3ZeSv8rSo85A2wtV69QXo2fFGnaxWq8gVpY0QN4t3lQ/n8hmPX5Aj8s4e3Z9R5nIc6hI53lIVSg313eqdGYVB6NDaeUda1w38owunRLf3K/cyr8t7ZIuE0anQ5pu589913GDlyJPLy8jB8+HDY7XaMHDkS99xzD6Kjo9GlSxdMmzYtkLISPqLGY2pVaGHi2EbQZJUmDil5h5ichT78CiTan0qpEECuhQlQl2PqJFqFB+I/hyqYr/NW5avJKaY+pr5Beja8qVKpa5WMTqvgf6hWfC96eUzdIiZqQvkNWdey3spI4J9q8t3Zar+ekXmMUL5VYOck1zbAUD63YfrOO++ga9eu2LFjB2bMmAEAGDNmDBYtWoSsrCzk5eWhffv2AROU8B21OaZixSXWPxa7d2snXxB7SMWtU5yRlkTOJHUg/JUlAEzvzvb4XpcS6RrT6uSyWD2u4Cwo2CCx2uc3TPmvo4anKusH0rPhjRrD1KFrPbf31rX+OwHEufxSU58AoImKPnTRITzimYfESD2ujGc/yF5keIIfyuT3AC88XOHXgkLKY8q6FmoaYCif+yr866+/cO+998JoNMJgcFyRNpvjy8vIyMD48ePx3nvvBUZKwi/UKEtWC5NYkVdOC2UJAKW1dpRb7DhbYYWpxu618nMaYc1VdMb3t+dfKPBMlwQ8kV6Le9vF4NN+TfFYpziM7xiHZf2beW2r0+mwZkAy97ErGS6aWpvAVU0s590Jpf6E4Qzp2fBGrcdUvLk4ImKxAxY/nQBltQIqLA5de6rMikqL2DCt+3eyCl3bEJwAb11Zi4c7xOLxTnH4tF9T3NsuBi/1TMSTV8V7bXtl0wisus1bB7O4LNYAE2MKVLnFzrXAZxVOWSVykht18VNUVBRiYhwJznFxcdDpdLh48aLr/VatWuHUqVPaS0j4jZqEfFYoP8ao8xhrabVrEz7o/aX06EWgbiXfXMXSXE3aQqgSbdThsXQrMjMdSnBEu1jZ7TMSjJhwZRwWHq6U3Q4AMlad93rt8Z9LEGs0YcFNTXF3RgxjLwdyOabh/4gKDUjPhjdqDFNxGz+D7lJY3a0VphZpUxVWAa1XeN/3TtxzTNU4ARqCYZoaJeD9G+q6sijp2kHpMWgVa0Auw6PpzoYz1cwI1eUrziOziRH/7/+SZQeUsAxTqUlhWkQvQw3uq7Bt27Y4evQoACAiIgIdO3bE+vXrXe9v3LgRaWlp2ktI+I2/oXzvVbz/Cfk8OFfyaio5K4IwFz4UkGsW7Y7U71ZlFfDcLvZ0ICdq+9ayRiES8pCeDW/8MUwj9J5hdUA6fKsl7qpDlWEaQhX29Qlv/1MpskutmPcnO9/fCTPHVGKR0qhD+bfffju+/PJLWCyOtjMTJ07Ehg0b0KNHD/To0QM//PADxo8fHzBBCd9RlffEFcqvnxYVaubBO2kIHlNf8FdZAo7m0HJI/eYCY8Qh0DBX8oGG9Gx4o0bXinVVhF6HCF39OwHcjeHmMfzRKTWjjBsSaqr5pVh0RDq6VW0VUFTj/aM7ht94b98QQ/nchum0adOwe/duGI0O9/PYsWOxaNEiXHnllejSpQvmz5+Pp59+OmCCEr6jyjC1eTfYF3ssHSs3LSSTR808+MYOqycfK4FfCZtc+xrGby4I0u1KGuJKPtCQng1v1ESnqkRWhkEHGEW3sVZpU3K4F1Sq8ZiqmXLVkIhn6NrhMilQamEVPgGO64B1LTREw5Qrx9RutyM/Px/x8fEeM7VHjhyJkSNHBkw4QhvUGKa1jEpRsWH65v5y3JAW+F6g4rAWIQ1rFd+9eQSiDOr63NXYBcSKvvfPj1dh5i+lzEr7ob9Go2BnHvNYDbGNSSAhPRv+qMnnF+vlCL3OS+dtz68JeEGnu+pI5ph376SikRqmLEfx6MxYfHna7P2GCix2AVOyTFieXcV8/zqJmoyGGJniugptNhu6d++OlStXBloeIgCoa2HiHS6IFd2J5RYBx91mrwcK8pjywwrlJ0TovNIwlBAruUqLHc/tMuGiRJi/oEb6RxLn0BHykJ4Nf/wL5Xtv89wuU8CjUxFu+l3cjk4OSiGvQ62eZfHD2WpJo1SOhqhnuR79ERERSEtL81jFE+GD1h5TAPj+rPIEDH+I0MPjepvbuwnXfm9wbtfQYBU/JUbqEatmniu8w0I782t9ztt1nzdNKEN6NrxRm+Ik1stGvc5rwW+pj1C+yEvLmg7H4t722oWvwwnW3amFYTo5S774VIqzlVbmdLhwhvupNXbsWKxatQrV1YE1SAjtUWWY2rynS7AmL2lxI8ohDmmNzoxVzOMZlhGD0VfIt/toqCQwRpUkROg9prrwIM4L9afI7agp8F71hgbp2fBFjZ5lbS8VIQp88ZPn33OuTUTHJtJZfq1iDXj1uiZIj1c10bxBo8Xz0FfPZ0mNIBnRCle4r6y2bdtCEARce+21eOCBB5CRkeHqt+fO8OHDNRWQANacrMK8vypwRaIRc3s3QTOVIzfMKvKeWPN4Wa0rVvoQclCDWFkmROix5NZmWALg0Z+KsfaUZz7P2gHJuK1VdEBlCmVYOaYJETrVBQpi74w/zprDJovvOzdSSM8Gj2qrgNn7SrG7oBbD28bg713iVXmv1RQ+AeyqfBafHlXuT+wP4vN2S47EnhGpAICkJble2/91P7UrE6OmpaEUatvxuXPEZEULFR0VQh1uw3TChAmuf7/11lvMbXQ6HSlMjSmqBSb8UgK7APxeaEFarAGvXKsuXK0mFFtr9/aYshL6Ax04kGsVxcqDauyFUnEMxRgfoUOVSstSXCilZgypGPKYqof0bPBYfbIKHx9yGIF/FFlwfYtI9E7lL/JU7TEVuUKNQdJhMiPjCU5Y+lctcgNMlDhSYuFOwQgHuA1T9ybPRP3xdYER7rbBR39WqDZM1bSTsNgAm+gGebRTPKYoNF/XmlZx0qs/VuFoY1euzaL0aB6td/UibZ9ogF6nQ5XKgQPi0L0/YcQj5DFVDenZ4PH3nZ467p+/lGLzkBbc+6sNxbJC+WMyYwMejRIjp2sJb57tloD/5db8f/bOOz6Kcvv/n9mWnmxIBRISIIHQe++gRKRJsYtKEUVBsUTFa0UUQfEKiCAKXvih8lUUqRa8SjM0uSKIlECkk4QkbHqy2fL7Y9llyzO7M7uz2U32vF8vXro7szNPdmfOfJ5zznOO5fVT7cMRal/nyw08sbWnShqWE0CwMO3fv783x0HwUGPwfCYmJiG/xmB0WG05PMm7M7HoIA7topXYm6cFAESqOLzajb8GJ8tjGqjFns3IZRzm9YjC09kaKGTA3O6myYvYdUv2kxhPStXkVxlwvcaAaBElaAIdsrP+Q5HIvD2x+dgOwpTjMKt9uFeFae94FXLLdCioMv1tHRsp8VDrMK+dryHSO0GFu1qG4KuzVWgbrcC0NmEQmV0nOSeuNywngOjs5aqqKvzxxx+4du0a+vXrh9jYWG+Mi7iBghNm7LZfqML6M5XoGqvCE+3DbULbYsKxLOMarpThnpYhWH/WszptfKhVMmwdEYfCaj2qdUYkhsqdhrVYIjTQQ/kAcE9aKCa2CAEHk1B1B3th6mnx5pOaWvQREQ4lTJCd9T0sD1atwYhFf5bh7+u1eLh1GIZa5bWLzRG0rwOqkAEZaiXCFZzXaoT2jFdh++2xuFppgFIGl3mJHLyftlXfkHEcVg5shGX9jZBz7MXBdU1DS5sS5cpYsWIFWrdujVGjRmHy5Mk4fvw4AKCoqAjNmjXD2rVrvTLIQEaIIzC3VIf7/luMzeer8frhUnx5xnbGLSZEUKt3NLAKmXfzn+Q3buzYYDmSwhUuzxXE2B7ooXwzChnntigFHHNMxS7osOfk9YZlMOsCsrP+AWtCv+J4Od45UobN56tx144i5Fl16REbXbBPszFPrt2NCgup+6yScZBxHJqGyQUtlvEDzeW3KG98l/5AUY0B16oaTnk+wbfA559/jjlz5uCWW27B0qVLbepmxcTEYMiQIdi4caNXBhnICMmp/uh4uc1r+1wpMSEmVh1TpYwTNA4W/RJV7n3QCSqGPRVTGDqQeKZjuKj97ctFuVvD1Axfez2CDdlZ/4F16b/ye6nN9qV/3bS9YnMEq/SOOaaA+06A4Umuq5KILR9HVlU4zcLFfblS1x5tSLZWsDBdtmwZMjMzsXr1aowYMcJhe+fOnXHq1ClJB0cI85heZpRzskZMiElvdDSwSpl7ofK0SAWyOkWI/pwrKJQvnMki88fsy0WJXWlsj6efDzTIzvoPQjygZ60K4ov2mDIK7APudby7Ny0UnWKULvdzVu2EBVlV4YhdlCx1R6+GZGsF3wJnz55FZmYm7/aYmBgUFRVJMqiGxJFCLV45VIKN/7iX0M4Spga7mRarhqU1nixgkd3IoRHZQAj/m5CAPWPjkSRgxadRZBYTy7hSKJ9NcrgCL3URPjmYe7gUJVoDvr9QhXt/LsKSv8pdf8gJlZ4U5wtAyM66h9FoxJdnKvHqoRLJqkHoBVy65VazePsJfZBI76RZkMpFhoePTEzAR/3VgkSnWNHrJ5HqesHY1BBRXtPVJytQozdi6V9lGLCpwOPzNyRhKnjxU0REBEpKSni3nz17lhL07bhaqcet265ZDBYHDnc0F9fGjbX2pEpnRJiVGA13Ym2MRqODwYwLlgnuFGEJL4m0UC0iTZeWN3JTyWMqjlgRS0bPl+uR8vlVyc7dkIxlXUB21j3Wnq7EUzdaOn56sgIn7k5ElIez1VoBodZyqzxRewdAlEpmWf0uBHdzTFMjzLbW9b5iPaaEOMJF5LzNOViClw6WSLa4rCHZWsG3wMCBA/H555+jpqbGYdvly5exZs0a3HLLLZIOrr7z/tEyG1H46J5i0cdg5S3ZX4DhDI+p2atqf63KOXGzZiXnaUK+9IaQnWMq+WkaDN5uH+uMhmQs6wKys+7xlFWf8UqdEatPet4tSUjOqDNhGiwy713hphPAjBBbKzYXn2SsOMQ2NJHSOjYkWytYbrz88ssoLCzE4MGD8cknn4DjOOzYsQOvv/46+vXrB6VSieeff96bY613HC+2DSnZr3gWAqs+uv0FyIrUa2pMVtXeWKpknCgvpsLDhHxvhNjZq/LJhPLhS2Hq6ar+QIPsrDRY5366i5AMKOuuePZCVux9p/QgxxQQZgMplO9dfCkOG5KtFXyZtmjRAj/88AMSExOxYMECGI1GLFu2DIsXL0anTp3www8/oGnTpt4ca71D7GVytkSHj/8ux5FCreU9ndHRMthf/Kxak+YOQFo7MWxayCR8TJbwkpsGyhseU9YxaVE+P758uHi6qj/QIDsrDa4u+V8uV+OTE+Uoqr5pIN2xIc48pqKF6Y3d3S335o1QPplVcfhSHDYkWyuqwH7r1q2xceNGaDQa5ObmwmAwIDU1lXKeeBCz5uhSuQ4DNhegUmcEB+DHkbHoGR/EDCfZX/z2ZUcAkzBtBcf+uwoZJ0oselrCRIJObQ6whBZHU3tefGksG1J4qa4gO+s5zszVupwKzNxrCv0vPlaOwxMSAJhslV5kVMtWmNpuExvKd8cJYP13eieUTyX2xeBLcVjpSU9TP0N05ycAUKvV6Nq1q9RjaXCIKVP2+ZlKy0PcCGDOgRL8d3Q8M5Rvf/GzPKbFllC+7fsqmTjPgMJDj6k3QuwkQcWRIrK+npQ0pPBSXUN21n2c2YiFR8os/3+pQo9v/6lCd5jy6WtciDClzNamGmFaYMpxnMce05tpU8I/Y72rkLQpsaF8ypASx/jmIdiQ650Oia5gOajqK6IuU41Gg3nz5mHgwIFo3rw5mjdvjoEDB2LevHm4fv266JPn5eXhscceQ8uWLZGQkIBevXph7969lu1GoxHz589HRkYGEhMTMXLkSJw4cUL0eXyFQcRMc9v5apvXhwtN+amCPKaMh79ZrNrXpXTfYyr4I8zPSwk5R8XRM16F9o1u1jj0RtMDPshjKh6ys57jrCPPhXJbt+ivV0y211VJPFaFE+BmPUrpckyFf856VyFRLQrle5dnO0bAXAQlWA50i3VdW1YqKlherHqKYNlw5swZ9O3bF4sWLYJOp0P//v3Rr18/6HQ6LFq0CH379kVOTo7gE2s0GmRmZsJoNOKrr77CgQMHsHDhQsTFxVn2Wbx4MZYtW4YFCxbgl19+QVxcHMaNG4eysjInR/YfxHhMCxjtxIxGo9s5pub37MtIup1j6sSgPdeRv04mx3GS53+SsRQHx3H4/vZYLOmnxoZbY/B4W3HdoDyBhKk4yM5KA58u1TPyq4pu5OOzPmLdlpSvIPpNW2t77BDRwtT0Xz57ObBxkMN7tqF8IecQKUzJ2IqiTbQSO8fE49991NgzNh6t1HUnTBuSx1RwKD8rKwvl5eXYtGkTBg4caLNt165dmDRpEl544QV8++23go63ZMkSJCYm4uOPP7a8l5qaavl/o9GI5cuXY/bs2Rg7diwAYPny5UhPT8eGDRswefJkoUP3GUIzPr4+W4k8Rr27vCoDc4ZeYac2WRekZRZvp46VolflOw/lz+4QjsRQ5xZR6UbuljPIVoonQinDg61MXaB+vFjtYm/poAL74iA7Kw18FmnaLkePs7nWKOtSrdAZEaUyWRy+tBRzy2d74RoiulyUcyfAq90iccvWazbvcVbWUFiOqaghka11gwy1Ehk3BKk3C6KEKTibtL6G5AQQ7Ds7cOAAHnvsMQdjCQCDBg3Co48+iv379ws+8bZt29CtWzdMnjwZaWlp6N9p/8MZAAAgAElEQVS/P1auXGnpH3v+/Hnk5+dj6NChls+EhISgb9++OHDggODz+BKhHtOlPN11zpTomDmm9gbSmcfUPpQvtr2oysXip9ZqJRqHOrd27tbl4yM+hIqWeoI3FqTxUa1ne6kINmRnJYJhcoqr9dh4zjH/70K5qbSUlnGdWttalp01vW/6r0OOqYuOfPaoXDgBWkUp0CrMVv12j7vpkRNi18Xa4p7xtmk/7aLdWpYSsHjT1iaE2B68IYXyBV9lUVFRUKvVvNvVarXT7facO3cOq1atwuOPP47Zs2fj2LFjeOGFFwAA06dPR35+PgDYhJzMr69e5e9MIybM5c7+YqiqDoa99med72hxKPPzJ89fRq3RUYRdzr+GHMXN70BT6Xiey/nXkKO8itwyGYBgy/uG2hqAM0LoT682ViEnJwdFBXIAjqGksLIraBFmRLwqGAVa0xgea6a1+Ttr9CFwNveura0V9TtEGYGUkGCcrzKd74Gmwj/vzd9barw11jyN7TUhNcEyI6oNN3/vv06fgYu5i9cR+l2mp6d7eSTOITsrHpPGtrWhpRoNcnJsvYsXqjgAjp33SrUGGIzsUP3ps/+gLNj0wL9czf786bP/oDLEiIJCJYCbQlFXdh2A8HxuZfk15OTkoboyCIDtDZMQZED++bN4vqUM046a7l0ZjJjRuAQ5OaYKA/klru/rS5cuQl0iPIoxNZ7DL5eDYbhhv2cnlSMnp9Tl58jOmigvtb0mpCSK08L6Oikqq0BOjvgmPlIilZ0VLEwnTZqEdevWYdKkSYiIsM0pLCkpsWwTisFgQJcuXfDaa68BADp16oTc3Fx8+umnmD59umU/+zJA5hWQfIh5sOTk5Hj1QaT6uwCosC2yzzzf3svMz4fHJkJzudDhfXVMLNLTrX6DY/kAbAtKRzYy7fP3uSoANy/W8JBgJEYqgGJhKwe7JUUjPT0STblK4IxjGGxY+xYIU8qwWnsGO7XxSA6TY1KrUJvFB7U8f58ZlVKF9PRkQeMxsyNZj09PViA2SIYpGWGC0hO8/XtLiTfHmp9XA/zleF0NSFRhT56W8QnhKDggTClHdc3Nh1/jlBY+9XLXp9+d7Kx4avRG4LcrNu+FRaqRnm4r4LmSWuCwY09yIziU8dTjb9osFS2jTI9Jg6YW+N3x842bpSAtSoGr54sB3EyTaRofC1xwLeLMDMpIQnqMCuoLRcB123Sb9rEhJhuZk4NNmTHYfbUGw5oGo2/iTWeBpkALHLtmf1gbkpOTkR4nXCynA9jWuAY7LlVjQGIQhjR1PaGtT/ebt8caU6wBrtp2IesWq0SlzogTGs+aQKQ2CscfpVbPcWUI0tObeXRMT5DyuxQsTNPT08FxHLp37457770XLVq0AGDq3bx+/XrExcUhPT0dGzdutPncuHHjmMdLSEhA69atbd5r1aoVLl26ZNkOAAUFBUhKSrLsU1hY6DC791cMYlY/MThUoMWRUscHun14nm9V/tdnK/HIblsxqZRxiBSx+qmNmr8Pc0q4HGE3jpUQZMTL7SMFH9dT4kPkeKlL3Z2vIcEKFb7VMwpPtAvH8/s1WHnC/XaOwXIOoUoORVYdNRtS7pO3ITsrHqb9Y4TlnZV5/OwS26tlHd7nyzGt0Rvx6O7r+MEud1tMyhQHoFXUjbxExucyrBbRDGoSjEFNHAWiNyqgAECfhCD0SXCMlhGuYaVOrB7cCCkRCqg/c+6wcUWC3doOse1Q/RnBwtR6dr148WKH7QUFBZg+fboldwkwzcL5DGbv3r1x5swZm/fOnDmD5GST5ywlJQUJCQn49ddfLbX8qqursW/fPsydO1fosH2Kp5fJ6lNsgWAfcuLLMbUXpYDJWEaIyH3KiDYncTt+prVamnyjlAjKGa1LWA8+8zXhaU5UkJxDmJ3yJWEqHLKz4nGWY2+Ns8vw88uuhSlfjunv17T4ilG7UoxQTAqXW1bxs+5BIbZWiBAWW/Sf8AzWbxkpUZ/uRLsoVEAW2N+yZYukJ3788ccxfPhwvPfeexg/fjyOHj2KlStX4pVXXgFgMrYzZszAokWLkJ6ejrS0NLz33nsICwvDxIkTJR2LtxDqME0MkTFX5fNhn2RfLdAwAyZjGSHwxkgKk6PVjTAWq8ZfokSJg290j5LkOIQwWM+mqBvXhNzDhWrBcs6hTA4JU+GQnRUPqyoJU5i6sQjP+lnPsrMAsPU8u8qFGI/p8KSbHlDW/WkvQtjnc749NUJOi5fqGNYlIMYx5Ix4u8VPDcnOCr5K+/fvL+mJu3btis8//xxz587Fu+++i6SkJLz00kuYNm2aZZ+nnnoKVVVVyMrKgkajQbdu3fDtt9865F75K6wJTLXOiGC7B7fYeY690WUZzOs17KMK9ZjGh8jw1a0xFu8ay+iJLSBtplm4HLPah2N/vhZjUkNsir8T3of14DNfE6xtahUHjVaY0QuSO14XDclgehuys+JhiVCt3oiKWgOC5Zyl97z9hF7ssat5St7lM2pQA8I9pk+0C8dLXW5+1yxBK2SFP+tzC3pFIbdUh9JaI57rGEGtm+uYGsal4W57b3tig+08poEYyvcGmZmZyMzM5N3OcRzmzJmDOXPm1OGopIMlGLt9k4+tI2LRPPLmVy+21KO14NUb2N1ILlfyG8sIARZz4/BYtI2+KRhZoXx3hWmogsMjbcLxSBu3Pk54CDuUb7omWL9zQogcGq2wRP1gOYdQub0wbTghpvpIQ7ezrNzPHy/VoOm6q0gKk+PrW2PQJlop2s4CtmKWz2NaWM3vBAiRcy4Ln7/V0zZixDKr9vcU+3yO7yWFyfFoHTbUIGwRY/tSI+Q4Vya84Hd0kJ3HNBDLRQHAsWPHsG7dOpw7dw4ajcYmzwkwGbgff/xR0gHWZ1iG7HKlHh8cK8PiftGW98SGmLQCjOWVCj5hKsxjGhtse9GzcmVC3UxIpDwn38J68EXcKCKuDnLc2ChYBpQIO3ZCqNzBu8O3aIRgQ3ZWHHxpSwBwqUKPJX+VY/mAaKeLn/jQWplRPltbwJOGpZSZuj+J7cjDmji66zEVmrZFeAcx0aL4YLHC1NRV0Xx56YymiZTY7l7+iOCr9j//+Q8GDRqEVatW4eTJk6isrERVVZXNv8rKSm+Otd7BZzDXnLb9nsQaTK3etTC9zCNMFTw5pr2sCimnRsgRZ5e/wso9FNtyzwwJU9/izGN6X1qojedlRtswvNhZePWD17tHOkxYKkiYCobsrHj4bKCZL8+Yvi+dG1VSbJwAIq9jhYxjRpWs768n2jl6M1nmUUgXKZbHNFKifEbCPZxNylcMiLZ5/Vr3SDzSJszyOshJWnGzcDlaRCocFpo2lCL7gj2mCxcuROfOnfHFF18gMTHRm2NqMDi7KPUGo9u5T7YJ+ex9+Po6q2SOq6YBYEk/Nf51sATVeiPe6B5lU4cUYBs91nGE4K6gJaTBWY5po2A5Vg9uhKXHypEaKUdWpwiog2R4vVsktl2oQv/EIJTWGrHqpGPFiIW9otApRoVQha1wakghJm9DdlY8fDbQHndyTIWE8vlQyRwXAgImQfLR8XK0jFLguU6Oebwsj1eYEI8p48YWkrZFeA9nGmB88xCcuF6L3/JrMDolBH0SVGijVqCkxoBzZXo82SEcLx4owSU7J1O7aAUW94uGjDNdX6VW9rVSZ4S6AVT2EixMS0tL8dxzzwW0scyr1OPR3ddxQlOLaRlhUMk4fHi8HOlRCqwcGI3kcOu8UaPT8iRN111BbLAc83pEOd2PhSezeCWPsWytVmLD8FjezzHDS+QxrZew1j+EWz34RqeEYHSKbYeb2R0jMLuj6SH63p9lDp9fPiAa96aZuu/YXxfPHyhBQqgcrx4qgUIGLOkXjX6J3rWe2Xk1eCpbA53BiGeayVA/yn2TnTWz60o1ns42dTR6s0cU1uVU4kCBFuObh2Bhb9uJsxDBeMvWAoxNceza5AqtgFX5fChlbBs5oUUoJrRgd/sD+NKmBAhTxo0dqSJb60ucLUhSyTm80cM2v7hRsBwrBzWyvH7vzzIHYfrDyDjLhMP+uvj3sTJEKDmsPV2JTjFKfDww2mGRVH1AsDDt3bs3zp49682x+D0fHS/HrqumyuFv/3Hz4VxYrcW7f5ZhiVXeqCsjVq035T/N+s2x1qgrPDGWCh5j6fJzrPCSm8I0nMJLPsW+QQMABw+5M+zzjwFbjzrrunjo15vdx57bp8G+cQmCz+cOWfs1yCkxLdiaf0aFB3o472TkL5CdNXWdemafBrk38u3u/+XmtfPpyQoMTwrG8OSb5ZWE2MDfr9XiWHGty/3sEZI2xYdCxrll61ifEDKZZzlHw8ljWq9h2lorO2Zvaz+xao7y38s1WPF3BV7u6r1GNPobDjipLzPBh1uwYAG2bNmCL774Anq98ATdhsSSv8p5t621yxt1lpBvTZkbYc5aD4ylSsYhOUyOxlZdI7rEui7XJGeFlwQK06c72OZRPdOxfpShaaikRigQY7Wis0+C8BaFANCWUQuxpVWVCVfXxQmNjimOpeT49ZtVBK7UyFBeT/Jcyc6aJt5nS/n/9iV/2XrshdpaVukeV9ikTYm8hlQy4PVuth6xVwSIBNaaAyETR7mMQ3rUzfuwZaQc9dBZ1qCYY9edcF4PcSLRujIOADQNldvknrqytazolpT8dKkaCWuvoNF/rqDn3hDc/98iSY4r2GPasmVLPPfcc5g1axZmz56N+Ph4yOW2Vz3HcThy5IgkA6uPaPVGqG7MbN0xgoLP40Hek1JmMmAf9I3G8/s1CJZzWNDLdYF7T+qYPtY2HEeKanH8ei2mZoShTTTVLfUlChmHxf3UmHOwBBEKDm/1ENfgoEecCrPah+M/pypgMBgwvW0EOsfc/E2FXBdFNQY0lqhBgz32q9gBETNwH0N21rVNs1+5LFSYuoNntpZD11glnmofji/OVKJrrBKTW/OH8FnnFMuiPmo8nX0dRiPwfh91vYgSNGT6xKswo20YvvmnCj3iVHggPcz1h6yY1d707Nx7tRpNwxUOv6m7lXGkwnoSZWT6+t1DsDBdtmwZXnnlFYSHhyMjIwORkdSn3J4zpTrLDMcT4+KKvXlaDN96DS90Ee95NOeKZiYHIzNZeB6bJ6H8hFA5Nmby568Sdc+olBCMciPnDjAJozd7ROHNHlHIyclBenqSzXYh10VhtfeEKcvjVF8qqZKdde2Z1NtNPLwpTF88UII9V2vwTq8ot9KmOM6UR2ifS+gMT6IJAxsH4fCEwM5P9ifkMg7ze6kxv5farc/Hhcix+bbYG3bWMVPevllPXWNf6lKqUlWChenSpUvRr18/rF+/HmFh4lR/oHBKU2sRpt40lgBw8JoWU3YW493e4i54d8vasRY/ubsqn2jYCLkuCqv0ALzjOWetvtbXE2VKdta1ZzKv0oAqndEyAeKrQCIV2y5U8y4adYbKzYe0t/8eouFQ62Wd4fL8dqeXyoEr+DAVFRUYP358wBpLIeRV3rQo3hamAFCiNeKXy+w+zXy4O6Nh1tbzcRiB8E+Eeky9BcvhVj8yTMnOAq6FqRFAoVWNqLqwtd+dq3KjAop753KnrBURmAhp3uDNBif2HlOpfFWCb50BAwbg6NGj0py1gWJ9kdTVrPd0ibBWkWbcdf0za+uRx5RgICTvyavClOUxdaO4ui8gOyssl9P6YevthXRmLpSLtLVulsWrC6FNNAyEdJYqFFro1w3sO65KFcoXLEwXLVqEgwcPYtGiRSgoKJDk5P5KqdaAfx0swaO7i3FSI7zEiHUh8boyLn8WiSuBIqSDCAt3a+sRgYeQlcBFXhSmzBzTevKsDyQ7CwB/X6/F9N3FeOVQCcpv/HBCPJPWD+SaOvpxj4i1tW7aRwrlE0IRIky9a2t9nGPapUsXGI1GvPXWW3jrrbegVCohk9mqFY7jcOXKFUkG5ktePlRiKf+060oNjt+VyCyXZE+lVSJbXc3ixZ4myE1hyrL9VCKPYCFkgu7dWTzLY+q100lKINlZncGIsT8U4tqNB2dFrRHv91ULun5shGkdVdWqK1tbV88Oov4jpKteXaZNySXSBIKF6bhx4wKm9IR1TdK8KgP2FWjRX0CnGhuPqZ+6aNydxUcxVk0FyvVAiKON2rVZuVDuRWHKuPX89HZ0IJDs7K6rNRZRCgCrT1XcEKYiPaZuCrkwBYcKL+bfuRvKvz89FL9cqbG8vr1ZsJO9iUDm6Y7heG5/idN9vGlrHT2m0hxXsDBdvny5NGesh5TXGqAX8GTzxSxeLEFuVugJknP4V5cIvPVHGeQc8O++7pW/IBo+cSFyPNomDB9bdSGx55RGXL6eGFiLRwz1JMc0kOxsKU/MWogwrZAgxzQlXI6/vXgdsiqZCGF0Sgh6xlXg4DUtEkNkeK1b4JUMI4QxsUUoPjxe7lDb1xox6YhiccgxlWhSTcFYAdzzczEetGqpyEelE2PZM06FY3cm4PrDTXB3S3H1I49MFN6+sWec8y4+7uaYAkBW50gcvTMBx+5MxIOtAnfVMOGaBb3VaB3FP++9XKnHuTLviAJWjilFR+sHfb/Lx/P7NS73q3KSY5rVKQLn7muMiw80drpAs1mE4/X5pojOPKwOaFKgknPYfnssDo6Lx8HxCWitpoYkBBt1kAzZd8Q73WfliQpm0xEpsHcC1Hm5KAC4cOECnnzySXTu3BnJycnYu3cvAKCoqAjPPvtsg+5Gsu2C67JMzhLyW0YpkByuAMdx6BorrgVkUphwN2eHGOdGzN28JzPNwhVoImI8RODiqsPXoM3eWdzDyjGtJw5TAIFjZzlGp5i/r+uQX+U6J85ZdKpzjBLqIBkilDJ0ddJuOSXc0Y7Fiujh2TvedXqXuyhkHFqplYh0t/A0ETCEKmQuQ+grnUSvPME+E8bdKIE9gq/6U6dOYdCgQdi0aRNatmyJiooKSy/nmJgYHDp0CJ9++qkkg6qv2HpMbbdZtSYXnecpRktGBzn/Sd3NeyIIsbhKGynRGr1SvcI+vATUn3JRZGeFUaHjX2hqPfkOdfLETmII0wilcPvYyIWtJYi6wtVz3TpnWUq81flJ8J312muvISIiAocOHcLKlSsdXMPDhw/H/v37JRmUL/HE5e0sIV9ldeGIrf8pZjGEK2Pp6xZmROAgJG3EG8Wf2Tmmkp/GKwSKnQUAowdtD5yF8q1tbaiTazBEzmFmu3DL6yfbh4uKKEUHkzAl/ANXwrSCld8kAfaHlWrxk+DDZGdnY9q0aYiPj2cKpeTkZFy9elWaUfkQT2rImYVptc6Iz07Zus5tZvFeFIcxLowleUyJukLIQ37F3+XYeUVc9zJXsLRufckxDRQ7C3g2WTDb2nNlOvyWp7XZZj03D3XiAVXKOLzZIxLf3x6LTzpU443ukaJaNkeryJYS/oErW/tHYS1WnSzHWZENeVxh7zGVSl4Ivg11Op3TNnnXr1+HXF7/cw89CS2aZ/F3/1zk0JGproRplAtjScKUqCuEpKy8c6QMd/xYhLWnpcuBYuWY1hePaaDYWcAzW1upM+JKhR4DNjnmKQcJ9JjKZaZoVJ+EIHSOMoDjOJQKqAsJmFovarT15KIiGjyubG2Fzohn95Vg0OYCnJdw0anPOz+1bdsWe/bsYW4zGo3YsmULOnfuLMmgfInWgydYhc6Ac2U67LrqmM9hPYsPE+HvbhIkzoUb4qLCLQlToq4QExZ9788yyc7LXpVfP0REoNhZwPPo1L+PlaGMISRt0qZceEztSQwRJvpDFBzSnVSdIIi6RKitLdcZsf5spesdBeKtzk9OVcyXX36J8+fPAwBmzJiBTZs2YeHChSguNpVOMhgMOH36NKZMmYI//vgDs2bNkmRQvsST+qOVOiPyKtkHsL5wnOWBDmkSZPF6cgBeSjOFqWZ3COf9jDWNgmWYmsHvcSFhStQVYkqTSVkEWscQof7sMQ1EOwt47jHdcYmdAhIkE2ZrWZq1W5wSrQQIzuggGQY1DhJVMYUgvIWIYhL46aJ0qVOOq/KlOa7TO/CJJ57Axx9/jJSUFEyYMAEXL17EW2+9hXfeeQcAMGHCBACAXC7HvHnzcOutt0ozKh9yqEDreicetAZ2fhtgO4t3lgcaFyzDb2Pj8cPFanSKUUFdYnpgvdYtEp1jVCitNeDlQyUo5QkjxQbLsLBXFPokqLAupxI77VbjuVtgnyDEIrY02cZ/KtEvMQjxAr1WfLA8pv4sTAPRzuoMRvzEIyyF4KxHuMrq8nFma1ltpmUch59GxmHjP1VICpfjzh1FzM/GBsugknPYOSYOm85V4dl9zrvvEIQ3EeNwOlxYi58vVaN/YpDHi6Ed6phK5PdyKkztV4TOnj0bEydOxObNm5GbmwuDwYDmzZtjzJgxSElJkWZEPuSj4+V46aBnBqaCJ0fJehYf6SS8pJBxSApXYFobk4c058ZwOI7DHc1NhflfOcQ/xpggGeQyDhNbhEJngIMwDZR2h4TvEVsWbfLO62gUJMMvo+OQyih+LhRmjqnbR/M+gWZnjUYj7vixEHvz3HcCOBOm1hMiZ3VJ+TKq1EEyTHYSdTIdV2Y5/tSMcBKmhE8RGwmduKMIgxoH4bvMGI80gbdyTEVb/6SkJDz++OOSnNzf8FSUAkBhNTskaT2Ld3YhCEk/7ZsQhO8Z7vhIFWfjmWWFNAmirnCnmUNxjQFbzldhVvsIt8/LzDH1Z5cpg4ZsZ/fmaT0SpQBQpTMwyvObUMmshamTUL4HD1ExhfgJwtu4k6K362oNzpfrPXIC+CTHFAgcD5tULbuKqtm+GaEPaSE/7Jwu7Id2nJ0RrmfPYqKB4W77W02NZ/7N+uYxBQLHzgLAnjzPi31X6PiroAYJTJsS4gR4sTPb1joTvIB0ZXMIQgjuhuSve2pr7W5CF2uvBeNSKj/xxBOCk+05jsOVK1c8HlRdU6o1OJR3cpffr7E9ASqBMwkhBq1jjAq3Ng3Cjsu2Bt5+Fq/396cx0aBxN5+ZJ+jApKhaj1KtEc0jb5qy+ljHNBDsLADkVepx8nqtx8fR1BhwpZLPCXDz/+0n69bIBUwGsjpF4J0jjhUjSJgS/oS7i5qrRRjG82U6hCg4mzUAjp2f3BqGAy6Fabdu3ZCamirN2fyQk5pajPuxEFd5jJxYNp/nWSkqoccUAG5JCnYQpvbegfpSIodomLByTBUc/wJBM/YtJvn4+VI1Hvy1GJU6Ix5qFYrF/aIB1M/OTw3dzgLA2tMVeDpbI8kkgU+UKjjTAiYzzkrzCXmIymUcBiSqsMcu9cBVIxMhopcgpMJdYSrU1r50UIOPjlcgWA58PLARxqaa1rs4dn6qoxzTyZMn484775TkZP7IW/8rlUyUOsPee9QiQo7cMkfXkEqgl4n10LefxUeKaWNCEBLDmow1DZPjvIvSUPYtJvmYe7jUsghmzelKzGofjrQopUNCPgAY/HyS1tDtLAA8+ZvG6+cQk9csNIrFsrVxdtGpJqEyG7HckmqcEnWIu04oIeUxi6r1+Oi4qQFKtR6Ytfe6RZjae0ylWpXvM+Uyf/58qNVqm3+tWrWybDcajZg/fz4yMjKQmJiIkSNH4sSJE5KPYwuPh1NqouxE4tu9opj7dYtVCToeywDbC9MxKSE2FQCeaCesFipBSAErxzQp3PXMS2h9y6PFtiFhc2OL+ugx9Rb+YmfrCjVjMv5AeqjDe2EKDhnRwsSjEFv7Qd9om9fv9mbbd4LwBnzVgFwhxAlwSmOb5mjdHc2xjqmPVuVLSXp6OrZu3Wp5bd1qb/HixVi2bBmWLVuG9PR0LFy4EOPGjcOhQ4cQEeH+il1fYW/IhicFY1GfKPz7aDkuVegxqHEQRqUE4/ZmwYKOx3Ld2+eYBis4fJcZi2XHy5EUJsfzPIn8BOENWA90IQXJ3TWy5Tc+x/KY+nuOqTfxBzsr1eJSV7BC7PN6RCFcyWHF3xWIC5ahV7wKM9uHI1RgNXCWrbU/z61JQfigrxo7r9QgMzkYveOFORgIQgpYHdCEIMQJwIoYaPVGqOQcY1W+W8NwwKfCVKFQICEhweF9o9GI5cuXY/bs2Rg7diwAYPny5UhPT8eGDRswefLkuh6qx9iLRhnHYWpGOKZmuOfFZAtTx6uia5wKqwY3cuscBOEJrGu0qQBhuvVCNfbm1aB/YpCo85mNM3lMbfEHO8sq4eUNIlSO15w6SIZ3eqnxTi+1W8cU4jHlOA4Ptw7Dw62d1z8lCG9Q7uYNNm3XddzSNBhqJx3SWHmohdUGNAmTey3H1Km+vX79ulfzns6dO4c2bdqgY8eOmDJlCs6dOwcAOH/+PPLz8zF06FDLviEhIejbty8OHDjgtfF4E7HFxl3BKqPnaqUoQdQlrPUfQvP6XnXSRIKPihtWkr0q33+VaSDYWW0dzQxCvbAcnjXRcbaoiiDqGvtUQTGsPlXhdDurSoq5Xrt9jqlUt5/PPKbdu3fHRx99hPT0dBQWFuLdd9/F8OHDsX//fuTn5wMA4uLibD4TFxeHq1evOj1uTk6O18bMIlhmRLXB9a/hybhYn71WKgNgG/avLLiEnErfPYDr+rt3h/owRjP1Zax84zQYgXhVMAq0JqPZU61HQVExAKXLY/6vsBYnTuW46L1smzt4pagEOTnXUFCodDjH5StXkSMg0z89Pd3lPvUJf7GzJ3POwv73EosQW2uorkBOznW3js/3N+VfV8H+UenLe7M+2IX6MEYz9WWszsY5MVqGrRdu6oHnW2qx8KywdJK5h0sxOiSPd3tusaPWOHr2IkKKDaisDoa1f/PKxQsIKXatQVzZWZ8JU/t+z927d0fnzp3xxRdfoEePHgAci04bjUaXhajFPFikuCAbhynwD2N1vT3uPvBycnKYn60o1AJHr9m816VVc0GhUm/AN05/oj6M0Ux9Gaurca6NqsEbh0sRpuDwTq8orD1dCVwsF4AEdDAAACAASURBVHRsWUIq0tVsEas3GIG9trU8uZAIpKc3QoSmBLhke474xMZIv7GSNJDwFzubnNoCOMD/8BOCEFsbHx2J9PRop/uwcHod5xYCsC3N56t7sz7YhfowRjP1ZayuxplmNGKeqhzf/FOF7nEqPN29CRaeFV7r2Nmx/z5XBfxdbPNeUExjpLcMBfdnHoCb92Ra8xSkRbl2PLjCb+IR4eHhyMjIQG5uriUfqqCgwGafwsJCh9m9r0kI8Y0QFJL3RBC+pndCEL6/PQ4bhsciLUopKqR7UsPf9IJVGNoSXmKE7f04kl+n+MrOShHKF2LfQiVOmQLcz98jiLqC4zjMbB+BX0fH493eatGpg84WQbFtLTttSqpV+X6jZKqrq5GTk4OEhASkpKQgISEBv/76q832ffv2oVevXj4cpSMJob75Cll5dO70JieIukSM3Tql4e8Q5MxYsnSEP+eY1iW+srNSaDsheZ3W5fGkgorlEw2ds6X8TgCWaOXLMa33dUxffvll7N27F+fOncPvv/+Ohx56CJWVlbj33nvBcRxmzJiBDz74AJs3b8bff/+Nxx9/HGFhYZg4caKvhsxEiMd0fk/pa9q1ilIg0moFar9EKk9C+D9TRKxavlbFr2ZYCfllWnO5KFqVb8Zf7KwUHlNX3lAO8MqqePsye96w5wQhNW90jxS877Uq/hSZKoYXrNRSAcX2/Trr/OQtrly5gmnTpqGoqAixsbHo3r07duzYgWbNmgEAnnrqKVRVVSErKwsajQbdunXDt99+63c1TBNDnQvTkc2CcR+jwLOnBMk5/LuPGq8cKkGEUoY3u5OxJPyftCgl/tUlAiv+rkBRjXM3WqkTN1s1w1hW6Jx5TMWNs6HgL3ZWaOtDZ7gSpm90j0RKhPSPtEGNgzCldRi+O1eF3gkqr9hzgpCaB1uFITuvBoeu1aLYpa3lvz9ZHlNzx70GV8d09erVTrdzHIc5c+Zgzpw5dTQi90gIYf8S/+oSgazOwmcs7jChRSgmtCAjSdQvsjpHIqtzJNSfXXa6n7Oi0axQvtlYrsupdNjm7y1JvYW/2FkpQvl8wnT/uHhk8CySkwKFjMP7fdV4v697dVAJwhdEB8nwf7fGAoBrW6vlv0GrGLa2SmdEea0BGq1duaiGlmNaX+HzmAZ7IQmfIAKJUifGkiVMtQbgSKGWuX+gekz9BW+G8lmNHAiCEI5Yj2mFzogP/3KsriKVx5SEqQcoZfw5pqw+4QRB3OTlrs4jCmI9pgCw41I1833Spb7F01D+3S1DEM3TnYaEKUE4p1208+C4U48pI22qstaAvXk1Du9LdS+SMBVAqIJD+I3ZulrFIUhuEp7LB0SjZaSCudKYVsgThHMebh2KrrH8IVhnxpJPmP6vkL2SX08Vf3yKk5/SBrWKg1IGhFvZ3LRIBV7oHIlWUeyHKwlTgnDO2z2joGa06zXjzAnA6ktSpTc6lPN7oGktZBJVsPBZjqk/ICTtbFNmLAY2VkFnNM36w5Qy1OiNMBhvthlNDZcj167ws9QtSAmioREbLMeOkXGo0huRtM6x05BTjymrXhqAP3hC+QbymfoUIR7TK5MaI1QhQ5XOCA5AkNyUN6ySc1DKON7JCAlTgnDOoCbBOH1PY2y7UIXJOx07o5U5SQJn5ZheLNdbyvOZmZnKX95PLAEtTF33awIah8rAcRyU3M1SCPbe0NZqpYMwJY8pQbhGLuMQzpMw78xY8omUPJ4SU+Qx9S1CFj+F3ug/az2pD7OqS5oWqQAHx7SMIN/0OCGIeoVKziFKxQ6Si3UC5NvZ2faNlJBS8gR0KF8nwFgK8Xy2YeRvUI4pQXhGtZ7f08YnTPkgXepbpFj8pJJzzJbLrtqnEgRhQsWjS8QuNLWnZaS0s8OAFqZOJgkWwgQI09aMUiXkMSUIz+HzmooVprQq37dIIUwBIDWC3KME4S58gs+dhabWhEu1HP8GgS1MRYSXnJGhZnhMKceUIDyGz2CyOj85I1DrmPoLtSJ/Lz5SvVBAnyACBT6N6anHVOoIcWALU6PrL1NI/lI6Y7UoPQgJwnP4DCbf4ic+ArUlqb8gnceUhClBuIueR5d46jGVOkIc2MJUgMdUSP4Sy6va2EWrUoIgXFPO6zElYVqfkEqYDm4SZPOaQvsEIRy+iR2fnQWAGgFOgGCJb8PAFqYSPqwW9bnZqz4zKQjJ4TSzJwihfNRfDdYUkE/Q2PdodgXlmPoWV04AoQ6X7nEqDGt6U5w+3SHCg1ERRGCRGqHAqGbBDu87K+cmpAax1CXbAlo9SdG/2czUjHB0jVWhqNrgMKsnCMI596WHoV0jJQZtvmbzPqu4MyDeA0epNb6hWmfEmksKnKp17BJjjYBUfgv/d0sMfr1Sg7hgGTrHqjwcIUEEFmuGNMKPl6px33+LLe/VOLGnQmwtCVMJEZJjKoYuZCQJwm06xagwslkwtl242VaU1acZEL+YhjymvqFab8SH51QAnAtTpYiSTwoZh1uTHL0+BEG4Ri7jMCLZ9v6pNZgm76zOTUKiU8ESL/YOOGFqNBqRna/FSU0tVp4hIUkQ/oT9zJtPmIr3mLo9JMJNdCK+dBKaBFF3cJyptbp1RKpGD4QwFKFWgBNA6sVPASdMAeD+/xZBozXCVYrtu72jnG4nCEJa7AtA84WYhPZeN0Oh/Lrn0DUtdl1x7ikFTAsnXukWWQcjIgjCTJCMs5n41+iNzDKXQjymIXJO0i4mASdMOY5Dm2gl9uWze2qfvicRm85VITlcjtuSQ+p4dAQR2ATZzRW1euBCuQ6/F2iRGCpHnwQVOI4TvfiJPKZ1T6soBUZsL2RuG9ksGM93jsD+fC0GNwlCi8iAexQRhE9RyTmbFeBagxG/X9PifJkOXWJVlntSSHQqiISp57SOUjCFaWZSEOJD5HikTbgPRkUQhL3HdNfVasw5qLGEnKa3CcPC3mqnq0hZUI5p3RMTLEdssAyF1Y5PrMzkYHSKUaFTDKVTEYQvCJLZ2tpXD5Vg/dkqAICCA/7v1hgMaxosKDoVouCAWunGFpDlojKiHVuIAoBSRt2aCMKX2OcqbTpXbZMHtfJEBSp1BjdC+RIMjhANqyseAKjI1hKET1HZ1R41i1IA0BmBj/8uh95gFGQ7qcC+BCSEsP/sUGojShA+xX4Wz+K0RudGHVNSpr4gIYRdeZtaNhOEb3ElJo8X6wQ7AKglqQREKNl/NhlLgvAt9rN4FqdKdKJD+eQx9Q2RKrZNJScAQfgWV1GLy5V6FNcIU6ZCWreLISCFKRlLgvBPhISETl6vFd0cg4Spb+BzApCtJQjfIkRM/lUsLHFUaqdeQApTPmMZJqb9CEEQkiMk9/B0iU58HVN3B0R4RISSnAAE4Y/YLzRlcfy6MGFKOaYSwGcsKZRPEL5FiIHTaMUvftKTy9QnRKjIY0oQ/oiQfP6CKmEt9ijHVALIWBKEfyIkx7RMa0St2BxTN8dDeAZ5TAnCPxHiMS1ilHpjQZ2fJCCcxyiSsSQI3yJkFl9aa4DYO5XqmPoGyjElCP9ESI6pUGFKOaYSIJdxTHFKxpIgfIuQWXyZ1kiLn+oJUbwLTQPy0UMQfoMQJ0CRgFX5Ms5UkF9KAtY6RDAMJglTgvAtQmbxZbUGmx7PQqA6pr6B5THlAARLXF6GIAhxSBXKD1dy4DjymEoCy2CSMCUI3xIswFhqDUB5rTihSbrUN/A5AKR+kBEEIQ4htlaIMF0/LEaK4dgQwMKUPKYE4W8IbVVZ5cJjOrNduM1ryjH1DSwHAFU/IQjfI2ShqSs7mxQmR9/EIIlGdJPAFaaMlfmhPIn6BEHUDVKt7syItl3XSTmmvoEcAAThn3Cil5A60lrtnfXzfqPEFi1aBLVajaysLMt7RqMR8+fPR0ZGBhITEzFy5EicOHFCkvPFBzOEqcQlDwiCEIeQvCdXKDhHTx3lmJqoazvLEqFlYleuEQQhOeUS3IcZaqUEI3HEL4TpoUOHsGbNGrRr187m/cWLF2PZsmVYsGABfvnlF8TFxWHcuHEoKyvz+JytGV9oKE/NPYIg6oYgCSxSSoQc9hkB5DH1jZ1l5ZJer6EfgyB8TZnIPH0WDdZjWlJSgkceeQRLly6FWq22vG80GrF8+XLMnj0bY8eORdu2bbF8+XKUl5djw4YNHp+X9YVSiIkgfIsUHtPUCIWDYQv0HFNf2VkAiFeRh5Qg/I1Sse3zGGQ0VGFqNoiDBg2yef/8+fPIz8/H0KFDLe+FhISgb9++OHDggMfnZX2hJEwJwrdIEXFPjVBAbmfZjAEeyveVnQWAxsGB/d0ThD8itrIJC1bkWQp82vlpzZo1yM3Nxccff+ywLT8/HwAQFxdn835cXByuXr3Ke8ycnBxB59YZgSBZCGoMJjEarzLg3NkzQodepwj9m3xNfRhnfRijmfoyVinHqTUAIbIQVBncnyQOCS5EYrkR67twkHMAxwGhsirk5Fx3+dn09HS3z+uv+NLOAsC0ZBlmHb+5BPiuxrV+eW3745hY1Idx1ocxmqkvY5V6nPfEyrC/INjtz8cojSg4fxYFVu8JHaMrO+szYZqTk4O5c+fi+++/h0ql4t3PPkfJaDQ6rYEn5sHyQkEuFuYGwWAE5vaKQXpaqODP1hU5OTn14mFZH8ZZH8Zopr6M1RvjfFVXjjkHS0R9Jj5EhoIqA6a3CcOoLk1tttWX79Ib+IOdNZ7OwfjmIfj2nyo0C5fj+T7xSIvyjqfFXerLNVIfxlkfxmimvozVG+NM0RuxraQIOy7XiPqcjDO1dV8yoBHSU0K8MkafCdODBw+iqKgIffr0sbyn1+uRnZ2N1atXY//+/QCAgoICJCUlWfYpLCx0mN27y+gEPR7p1RhyjlrkEYS/MKNdOB5sFYp2X+VBo3UdbmoRIcf+cQmo1BmhlmL1VAPCH+wsxwGrBzfCoj4GhCs5KAXWqiUIwnuo5By+ujUGfxbVYvCWa4I+M6t9OF7sHAEACPNieU2fCdORI0eiS5cuNu898cQTaNmyJZ555hmkpaUhISEBv/76K7p27QoAqK6uxr59+zB37lzJxsEqAE0QhG8JU8oQHSSDRqt3ua9Kzln+Ebb4i50FgGiaNBCEX8FxHBJChfcHVsm8K0jN+EyYqtVqm9WhABAaGoro6Gi0bdsWADBjxgwsWrQI6enpSEtLw3vvvYewsDBMnDjRF0MmCKIOkQtsW0keOH7IzhIE4YwQERP6urK1Pl385IqnnnoKVVVVyMrKgkajQbdu3fDtt98iIiLC10MjCMLLCM2uCaNqGh5BdpYgAhcx3fbqytb6lTDdtm2bzWuO4zBnzhzMmTPHRyMiCMJXCJ2cU5k3cZCdJQjCTLDwSH6dNSGipB+CIPwSmcBQPglTgiAI92BV3+CzqHW1SJyEKUEQfolQvUnClCAIQjqCecL7dWVrSZgSBOGXCE19ImFKEAQhHUE84X0SpgRBBDSsVfkssRpCwpQgCEIy+BZEkTAlCCKgse93D7BXhYZRcwyCIAjJ4KsJTcKUIIiARqh3tK5WihIEQQQClGNKEATBgBXKZxlGMQWiCYIgCOeoeJQhCVOCIAIalt5kGcYw8pgSBEFIBuWYEgRBMBAqTMljShAEIR38wpTqmBIEEcC0Uisd3mMZRioXRRAEIR3tGznaXg7iukR5AglTgiD8ktkdwqG0slAf9VczFz9RKJ8gCMJ9Puyvtvy/SgY81zHCYZ8QBcfsEuUNFHVyFoIgCJHEhcixY2QcvjxTiY4xStybFor/Xq5x2I9C+QRBEO5zf1ooAOBYUS3uTQtFQqija7QuzSwJU4Ig/JbOsSp0jlVZXrPC9qFKCvwQBEG4C8dxeCA9DEj39UhMkEUnCKLewBSm5DElCIKQFJmdWWU1N/HauevsTARBEB7SPU5l8zo2WIaUiDrKyCcIgggQRqcE27y+JSmYZ0/poVA+QRD1hoktQnC5Qo8fLlYjOkiG5zpFQGE/tScIgiA84v0+aoQrS3Hyei06NFLijR5RdXZuEqYEQdQbOI7D7I4RmM1YNUoQBEFIQ0ywHMv6R/vk3BTKJwiCIAiCIPwCEqYEQRAEQRCEX0DClCAIgiAIgvALSJgSBEEQBEEQfgEJU4IgCIIgCMIvIGFKEARBEARB+AUkTAmCIAiCIAi/gIQpQRAEQRAE4RdwGo3G6OtBEARBEARBEAR5TAmCIAiCIAi/gIQpQRAEQRAE4ReQMCUIgiAIgiD8AhKmBEEQBEEQhF9AwpQgCIIgCILwC0iYEgRBEARBEH4BCVOCIAiCIAjCLyBhShAEQRAEQfgFJEwJgiAIgiAIv4CEKUEQBEEQBOEXkDAlCIIgCIIg/AISpgRBEARBEIRfQMKUIAiCIAiC8AtImBIEQRAEQRB+AQlTgiAIgiAIwi8gYUoQBEEQBEH4BSRMCYIgCIIgCL+AhClBEARBEAThF5AwJQiCIAiCIPwCEqYEQRAEQRCEX0DClCAIgiAIgvALSJgSBEEQBEEQfgEJU4IgCIIgCMIvIGFKEARBEARB+AUkTAmCIAiCIAi/gIQpQRAEQRAE4ReQMCUIgiAIgiD8AhKmBEEQBEEQhF9AwpRosHTo0AEdOnTw9TAIgiAwY8YMqNVqnD9/3tdDsWH+/PlQq9X4/PPPfT0Ul3z++ef1ZqyE+wScMFWr1aL++csNMHLkSJtxxcTEICUlBd26dcODDz6I1atXQ6PR+HqYHmH/3Tdq1AgpKSm47bbb8Nlnn0Gv1/t6iKI4f/685W9p3LgxSkpKePft1q2bZd+ff/65DkfpiPW4hf7bs2ePT8dMEN7C1bX/0Ucf+XqIdYpZHDr716xZM4+OPX/+fIlH7V06dOgAtVrt62E0GBS+HkBd88ILLzi898UXX+DixYu49957HW4of/O4mcdoNBpRXl6OixcvYt++fdi8eTPmzp2L9957DxMnTvT1MD3C/Bvp9Xr8888/2Lp1K/bv34+dO3dizZo1go+zefNmbw1RFAqFAlVVVfj6668xbdo0h+179uzB2bNnoVAooNPpfDBCW6Kiopj3yfLly1FaWorHHnsMUVFRNtvcfRARRH2BdU8AQI8ePQR9/rXXXsPTTz+NJk2aSDksn9G+fXuMHDmSuS04ONgr5xw1ahR69OiBhIQErxyf8A8CTpjOmTPH4b29e/fi4sWLuO+++zBgwAAfjEo4rDHW1tZizZo1+Ne//oVHHnkECoUCd9xxh49G6Dn2v9Hx48dxyy23YNOmTcjOzkbfvn0FHad58+beGJ5oOnTogPz8fKxZs4YpTNeuXQuVSoXBgwfjp59+8sEIbVGr1cz75IsvvkBpaSlmzJiBlJQUH4yMIHwH654QQ2JiIhITEyUaje/p0KGDx9+JWKKiohwmxUTDI+BC+UIoLy9HXFwchg0bZvO+TqdDs2bNoFarsWzZMptt27dvh1qtxttvv23zfn5+PrKystCpUyfEx8ejefPmuOuuu7B3717JxqtUKjFt2jS8++67MBqNmDNnDqqrqy3btVotVq5ciYkTJ6J9+/aIj49HSkoKxowZgx9//NHhb8zIyEBycjLKy8uZ55s3bx7UajU+++wzy3t79uzB3XffjXbt2iE+Ph5paWkYPHgw/vWvf8FoNHr097Vr1w79+vUDABw+fNhyPrVajRkzZuDkyZN44IEH0KJFC6jVahw9ehQAf45pTU0NFi9ejH79+qFx48ZISkrCLbfcgrVr1zLHqlar0aFDB2g0GmRlZaFdu3aIiYkRHMKTy+W4//77cezYMfzxxx82265fv44tW7Zg5MiRiImJYX5+9+7dePLJJ9GzZ08kJycjMTERvXv3xttvv42qqiqbfS9cuIDU1FQ0a9YM586ds9mm1WoxePBgqNVqbNmyRdDYhbJ7927cc889aNmyJeLi4tC+fXs8++yzyM/Pd9jXnJZy7tw5rFy5Er1790ZiYiI6dOiARYsWWX6Db775BkOGDEGTJk2QlpaGrKwsm+vajPn3KSkpQVZWFtq0aYOEhAT07t0bn3zyicfXH0EIwZWdcJZjeuTIEUyZMgUZGRmIi4tD69atMX36dOTm5jrsaz7Onj17sGnTJgwdOhSNGzdGamoqJk+ejMuXLzPHd+TIEUyYMAFJSUlITk7G2LFjceDAAWm/BB5yc3Px5JNPokuXLkhMTERKSgp69uyJJ554AhcvXrT8XU888QQAYMGCBcxUIb4cU3Movba2FgsWLEDnzp2RkJCA7t2720TZPvnkE/Tp0weJiYlo27Yt3n77bRgMBofxrlu3Dg888AA6deqExMREJCcnIzMzE19++aXNfua0J/PfYD1me29yfn4+XnzxRXTt2hUJCQlISUnBuHHjsGvXLg+/3YZHwHlMhRAeHo7u3bvj4MGD0Gg0ltyR//3vfygtLQUA7Nq1y3ITmV8DwKBBgyzvnT9/HiNGjMCVK1fQr18/jB8/Hnl5efjuu+/w888/44MPPsCDDz4o2bgfeOABLFy4EJcuXcKePXtw6623AjCJnxdffBG9evXCkCFDEBsbi7y8PGzfvh133303PvjgAzz88MMATGHnhx56CAsWLMDXX3+NyZMn25xDp9Nh3bp1iIiIwJ133gkA+Omnn3D33XcjIiICI0aMQNOmTaHRaHD27Fl8/PHHeOONN6BQeOdS++effzB8+HC0bt0a99xzD0pKShAaGsq7f21tLSZMmIC9e/ciLS0NU6ZMgVarxdatW/Hkk08iOzsbK1ascPicVqvFmDFjUFpaiuHDh0OlUokKyU2aNAmLFi3C2rVr0aVLF8v769evR3V1NR566CH83//9H/OzixcvxunTp9GrVy9kZmaiuroa+/fvx8KFC7Fnzx5s2bLF8v02a9YMy5Ytw/3334/Jkyfjxx9/hEqlAgC8/PLLOHLkCKZPn47Ro0cLHrsrPvjgA7z++uuIjo7G8OHDkZCQgOPHj2PVqlX4/vvvsWPHDjRt2tThc6+88gr27duHzMxMDBgwAJs3b8abb76JmpoaREZGYsGCBRgxYgR69+6NH3/8EZ988gn0ej3ef/99h2PV1tbijjvuQGlpKSZMmICamhps2rQJWVlZOHPmDBYsWCDZ30sQfLhjJ7766is8/vjjUKlUFvuZm5uLb775Bj/88AO2bt2Kjh07OnzOfH/dfvvt6NevH37//Xds3LgRx44dw2+//YagoCDLvgcOHMAdd9yBmpoajB49Gi1btsTx48cxevRoDBw4UPLvwZqrV69i6NChKC8vx7BhwzB69GhotVpcunQJW7ZswZ133onk5GSMHDkSJSUl2L59O/r164f+/ftbjiE0VWjKlCk4cuQIbr31VhiNRnzzzTd46qmnIJfLcfToUWzcuBGZmZno378/Nm/ejIULFyI0NBSzZ8+2Oc5zzz2H1q1bo2/fvkhMTERRURF++uknzJgxAzk5OXj11VcB3Ex7Mqc5Wad7WI/5+PHjGDduHK5du4ahQ4fi9ttvR3FxMbZt24Y77rgDS5YswaRJkzz5mhsUJEx5GDhwIPbt24e9e/di1KhRAG6KzyFDhiA7Oxu1tbVQKpUATB6j0NBQm3yjp59+GleuXMGLL76IF1980fL+zJkzccsttyArKwtDhw5FUlKSJGOWyWTo27cvvvrqK/z+++8WYapWq3Hs2DEHcaDRaJCZmYnXX38dd999N0JCQgAADz/8MBYtWoTVq1c7CNNt27YhLy8PU6dORXh4OABYPI1btmxBp06dbPYvLi72WJSeOHECv/32GwCga9euNtv279+PZ555xmIoXLF06VLs3bsXQ4cOxfr1621E22233Yb169fjtttuc0iFyM/PR0ZGBn744QenwpePZs2aYciQIdiwYQPmzZuHsLAwAKbvLiUlBYMGDeIVposWLUJKSgo4jrN5f+7cuXj//fexadMmTJgwwfL+yJEj8dhjj2HFihV49dVX8c4772DLli1YuXIlOnXqhHnz5okePx+//fYb3njjDfTo0QNff/21zQKA9evX47HHHsMLL7yAdevWOXz2+PHjyM7ORnx8PADTfdGjRw8sXboUkZGR2L17tyUdw+xpWLduHebMmYO4uDibY+Xl5SE1NRXZ2dmWB/KLL76IIUOG4OOPP8b48ePRq1cvyf5uIvBgLchJSEjAlClTLK/F2onc3FzMmjULSUlJ2L59u42I3bNnD+644w7MnDkTu3fvdvjsL7/8gl27diEjI8Py3rRp07BhwwZs27YN48ePBwAYjUbMnDkTVVVVWLNmDcaOHWvZ/5NPPkFWVpawL8COY8eO8S5S6tChg+W5uWnTJmg0Grz11ls2zhzAFL2qra0FYMofNQvT/v37u5UmkJ+fj+zsbERERAAwrckYPnw4XnrpJcTHxyM7O9tiO2bOnInu3btj6dKlmDlzps1zat++fQ6pYDU1NZgwYQKWLFmCqVOnomnTppa0J3OaE2vMer0eDz30EEpKSrBlyxYbwZ2Xl4dhw4YhKysLmZmZFlsY6FAonwez59Pazb5r1y6kpaXhvvvuQ3l5OX7//XcAQEFBAU6cOIE+ffpYhM7ly5fxyy+/oEmTJnjmmWdsjt2uXTtMmTIFNTU1vGLEXRo3bgwAKCwstLwXFBTE9Fip1Wo88MAD0Gg0+N///mdzjFGjRuHYsWOWv9GMOXxvLVhlMtNlxDLEjRo1Ev03zJ8/H/Pnz8e8efPwyCOPYMiQIaiqqsKoUaMsIX0z8fHxvIsSWJgF0ltvvWX5rQDTzNcsbvkWWL355ptuiVIzDz74IMrKyvDtt98CMHkxTpw4gQcffNBBdFqTmprK3D5z5kwApgeUPXPnzkWXLl2wYsUKrFixAjNnzkRkZCQ+++wzm7/bU1asWAGj0Yh///vfDqtS77nnHnTs2BHff/+9JdJgzXPPPWdjiFNSUtC7d29UVVVhypQpNg+GqKgojBgxAlqtFqdOU44gyAAAIABJREFUnWKO5dVXX7XxEsXExFjuPX+prkHUXxYsWODwb/Xq1Q77ibETq1atQk1NDd5++20Hz+qAAQMwYsQIHD16FCdOnHD47KOPPmojSgHgoYceAgAbe37gwAHk5OSgV69eNqIUAKZOnYoWLVoIGqs9f/31F/M7WbBgAbZt22bZz9nzISgoyOLgkIJXX33VIkoBoGfPnkhNTUVpaSmeffZZmwltSkoK+vTpg6KiIly5csXmOKz1CUFBQXjkkUeg0+mYEwU+fvrpJ5w5cwZTp061EaWAKe941qxZqK6uxqZNmwQfs6FDHlMeevTogbCwMOzcuRMAUFVVhUOHDmHSpEkW0bpz50706dPHIl4HDx5s+bw5z7F3795MITB48GAsW7YMf/75p1fGby9kTpw4gSVLliA7Oxt5eXmoqamx2X716lWb19OmTcN3332H1atXo3v37gBMYfNdu3ahV69eaN++vWXfu+66C5s3b8awYcMwbtw4DBgwAD169HB7gYw57MpxHCIiItCpUyfceeedlnQDa9q3b28jRpxRVlaG3NxcxMfHo02bNg7bzb8r6zcJCgryuELD7bffjvj4eKxduxaTJk3CmjVroFAocP/99zv9XEVFBVasWIEtW7bg7NmzKC8vt8mbtP/tAEClUuGzzz7DwIEDLd761atXu/0Q4uPAgQNQKBTYsmULM29Vq9VCr9cjNzcXnTt3ttnGCk+aF4ewvmvzNvuHCGBKQWF5RM0TGfP9SBDuIqQcn1g7Yc7xzM7OZtqda9euAQBOnz7tYLPs7ycAFgeE9VjNx7Wf1AMm0di7d29mLqsr7r33XixfvtzlfiNGjMCbb76JrKws/Pzzzxg2bBh69OiBdu3aWUSrVPDZlHPnzrm0Kdah94sXL2Lx4sXYuXMnLl++7JDLz7K5fJh/40uXLjE9zObv/vTp04KP2dAhYcqDUqlE3759sWPHDly5cgWnTp1CTU0NBg0ahPj4eLRt2xa7d+/GnDlzLMLUOlfH7CHic82by12wPEmeYL5hrBfSHDp0CGPGjIFOp8OgQYMwYsQIREREQCaT4dixY9i+fbuDUO3fvz/atm2LjRs34u2337YsdjIajQ7h/VGjRuGbb77B0qVL8eWXX1o8jm3btsULL7zgMEt3hZh6rGJCH65+k9DQUERGRjJ/k7i4OKdeTSEolUrce++9WLx4MQ4cOIDvvvsOw4cPd7pSt7a2FmPGjMHhw4fRtm1bjB8/HrGxsZaw04IFCxx+OzOpqano1KkT9uzZg6SkJEnzSs0UFxdDp9O5zOFkLaSz9myYkcvlLreZQ3/WxMTEWLZbY/aQSH2fEQQLsXaiuLgYAPDhhx863a+iosLhvcjISIf3zPeAdc1n87Vvn/5ixtvh4+TkZPzyyy9YsGABduzYYfGmxsfHY/r06Xj66aeZ9647OPtOhNqUc+fOYejQodBoNOjTpw+GDh2KyMhIyOVyXLhwAV9++SWvzWVh/o03b97stIQh6zcOVEiYOmHgwIHYsWMHdu7ciVOnTkEul1tKNQ0cOBCrVq1CeXk5du3ahUaNGtnM1sw3SEFBAfPY5tXKrBvJXQwGA7KzswHY1tZ77733UFVVhS1btjiUmnr//fexfft25vGmTp2KZ599Fl9++SWmTp2KL774AtHR0cxSVMOGDcOwYcNQVVWFw4cP4+eff8aqVavw8MMPO+TVSImYh4Cr36SyshKlpaXM9ANPRamZhx56CEuWLMGUKVNQWVnJ9AJbs337dhw+fJjpncjLy3MqCD/88EPs2bMHMTExuHTpEt544w1J80sB03daW1trWZXqK4qKiqDX6x0ecGaPk5T3GUHwIdZOmK/Lf/75B9HR0d4YkuUc5nvBHj57KCXp6en49NNPodfrcfz4cezevRuffvop5s2bB4PBgOeff97rYxDKsmXLUFxcbFlEas2GDRscVua7wvz9r127FmPGjJFsnA0ZyjF1gnWe6a5du9C5c2dLHt3gwYNRW1uL//f//h8uXryIAQMG2Bgls0g9cOAAtFqtw7HNXlZWOMZd1q1bh0uXLqFJkyY2QjA3NxfR0dHMGq3mRUUs7r77bkRGRuI///kPNm/ejMLCQtx///1OiyeHhISgf//+eP311/Hmm2/CaDTyCt+6JiIiAi1atEBBQQFOnjzpsN2cNyTlb2JPixYt0L9/f1y+fNlSpsoZ5jAPy6A5++0OHz6MN954A82aNcO+ffvQoUMHLFu2zKE8mKf06NEDZWVlOHbsmKTHFYtOp2OWvjF/R6wQH0H4GrMDwexQ8AbmBakse2EwGLB//36vndseuVyOjh07YubMmdiwYQMAYOvWrTbbAfi0y587NtfZuM2/8b59+6QaYoOHhKkTOnTogNjYWPz88884evSoTQ5pv379oFAoLKVrrLcBplyfYcOG4fLly1i8eLHNthMnTmD16tUICgrCXXfd5fE4a2trsWrVKmRlZYHjOMyfP99GPDZr1gzXr1/HX3/99f/ZO/M4J+r7/7+S7H1zLLsgN0QBDxRUELxACuKFa7Fq/VW/qNWuVKVVrFgsVbSCgkgVadV6oKi1FFQEqXIIciMiIHKEa4G9r+yZ7OaY3x8h2WTymZnPJDM5dt/Px8OHbDKZfJLMvOc17zPgdUuWLMG6desk95uRkYE77rgDhw8fxsyZM2EwGILC+IAn17a5uTnoca9XWK8pIKHgbckxc+bMgPBNfX09nnvuOQDQtIUXiwULFuDDDz/Ehx9+qJhj5c17Eo/8PHnyJGbNmsV8jdVq9f1O77zzDrp164b33nsPGRkZKCwsZOZohoq3ynbatGnM/ol2uz1iBtnbaspLdXW17/xUyuMliGjw4IMPIikpCTNnzmTmGLpcrrDH/Y4YMQJmsxk7duwIKrD517/+FVJ+qRp2797N7GfMuj54U9DOnDmj65rkkLK569atw5IlS5iv8a6bFTm64YYb0L9/f7z77ruSTpq9e/f6Qv4EhfJlMRgMuOqqq7BixQoAgT1KMzMzMXz4cJ+Xxv85L6+88gquv/56vPDCC9i0aRMuu+wyXx9Tm82GhQsXqm4V9dFHH/ma8zc1NeHUqVPYtm0bKisrkZOTg9dffz0op7OwsBDr1q3DxIkTceuttyIrKwt79uzB9u3bMWnSJNlqwAceeABvvfUWysrKcO2112LAgAFB28ycOROnTp3C6NGj0bt3b6SkpODAgQNYt24dOnfu7KsUjQWmTp2KtWvXYu3atRg1ahQmTJgAh8OBlStXoqSkBHfeeafuU7MGDhyIgQMHcm17/fXXo3///njjjTdw8OBBXHTRRThz5gz+97//Yfz48UwD/sgjj+DUqVOYPXu2r3BtwIABWLBgAR544AHcf//9+PLLLzXJ67r66qsxe/ZszJo1C8OHD8cvfvEL9O3bF3a7HadPn8bWrVvRu3dvTQdKsMjPz0dLSwtGjRqFiRMnoqWlBV988QXKy8vx0EMPUasoIiYxm8144403MHXqVFxxxRUYN24cBgwYAJfLheLiYuzYsQMtLS04depUyO9hMBjw2muvoaCgAFOmTAnoY7phwwaMGzcOa9euVb1fuXZRgKddYkpKCv7zn//g7bffxhVXXIEBAwagc+fOOH36NFavXg2j0YhHH33U95rLL78cGRkZWL58OZKSktCzZ08YDAbccccdERt7fP/992Pp0qWYMmUKbrnlFnTv3h0HDx7E2rVrUVBQ4Ouq4s+YMWOwe/du/OY3v8H48eORkpKCXr164c4770RiYiI+/PBD3Hbbbfj1r3+NSy+9FEOHDkV6ejqKi4uxb98+WCwWbNq0KaQuNu0REqYKXHPNNVixYgVSU1ODLm5XX301duzYgZ49ezKrnfv06YNvv/0W8+bNw5o1a7B9+3akp6dj9OjRePTRR0Maf+rNbzEajcjIyEDXrl0xYsQIjB07FrfddltQyx4AGDduHD755BPMmzcPK1asgNFoxPDhw7Fy5UqcPHlSVpied955uOyyy7Br1y6mtxQAHn/8caxatQp79uzx3WX26NEDhYWFePjhhzXr06oFSUlJWL58ORYvXoxPP/0Ub7/9NoxGIwYPHoynnnoq5pocp6en44svvsCzzz6LzZs3Y9u2bejbty+mT5+OqVOnBhnJf/7zn1i5ciXGjx/vayflZfLkydi0aROWLFmCF198ETNnztRkjY888ghGjhyJf/zjH9i2bRvWrFmDjIwMdO/eHbfffruvn6KeJCYmYsWKFZg9ezaWLVuGmpoa9OvXD48//jh++9vf6v7+BBEq3ol8ixYtwsaNG7FhwwakpKQgPz8f48aNU108ymLkyJH46quvMHv2bKxbtw7r1q3D8OHD8eWXX2LdunUhCdOffvopKArnT2FhIVJSUjB58mQ4HA6fx7a5uRl5eXmYMGECpk6diuHDh/tek52djaVLl+LFF1/E8uXLfUWTI0eOjJgwveCCC7By5Uo8//zz+Prrr+FyuXDBBRfggw8+QHZ2NlOYPv7446ivr8fq1auxcOFCOJ1OjB49GnfeeScATyHwli1bsHjxYqxevRoff/wxBEFAXl4eBg0ahEceeQRmszkiny8eMFitVprXR0jS1NSEIUOGIDU1FT/99JNuE5wIIlRycnLQq1evqOe5EgRBEOFDOaaELO+88w7q6uowZcoUEqUEQRAEQegKKQ0iiLq6Orz55psoKyvDhx9+iG7duuF3v/tdtJdFEARBEEQ7h4QpEYR3rnFycjKGDh2KuXPnIjs7O9rLIgiCIAiinUM5pgRBEARBEERMQDmmBEEQBEEQRExAwpQgCIIgCIKICUiYEgRBEARBEDFBhxamFosl2ktQJB7WCMTHOuNhjV7iZa3xsM54WGN7Jh6+/3hYIxAf64yHNXqJl7XGwzq1XGOHFqYEQRAEQRBE7EDClCAIgiAIgogJSJgSBEEQBEEQMQEJU4IgCIIgCCImIGFKEARBEARBxAQkTAmCIAiCIIiYICHaCyAIJepb3Xh1fwNsTgF/uCgT3VJN0V4SQRAEwcn28hb8+1gzLu6ShHvOTYPBYOB6nbXFjVf2NUAA8IcLM9A5xYSjdQ784+cm9Eg34ffnZyDJxLcvIn4gYUrEPL/fXIsviuwAgB0VrVh/c7cor4ggCILgoaTJhRu/qoJLAIBmJJkMuGtgGtdr799Yg3XFLQCAvdUO/Hd8F1y/ugpVdjcAoKHVjVmXZuu0ciJaUCifiHm8ohQAfqhyoLzZFcXVEARBELzM29twVpR6KPyulvu1XlEKAJtKW/Blkc0nSgFgwf5GTdZIxBYkTIm4w+Zv5QiCIIiYpajRGdLrBCHYzlfa3IwtifYGCVMi7nCRbSIIgmjXsNwPrMee/b5O76UQEYaEKRF3uBh30gRBEETsEWppkpth5lmWf8H+RpQ0UXpXeyKqwrShoQFPPfUULrjgAuTn52P8+PH44YcffM8LgoAXX3wRgwYNQn5+Pm688UYcPHgwiismIo2bIUId5DElCIJo17CEKesxwFP1T7QfoipMH330Uaxfvx6LFy/G1q1bMWbMGNx6660oKSkBACxcuBCLFi3C3LlzsX79euTm5qKgoAANDQ3RXDYRQZwMEdoqZZ0IgiCIqLH6lA0vH0vEN2faCla19JjOkgjb0xWhfRE1YWqz2fDFF19g1qxZuOqqq9C/f3/MmDED/fr1wzvvvANBELB48WJMmzYNkyZNwpAhQ7B48WI0NjZi2bJl0Vo2EWFYdU4tcVb89GNVK5Yfb0YDuXoJgminbC1rwa/X1eDT0kTc/k01fqhsDWt/bobcJBPaMYiaMHU6nXC5XEhJSQl4PDU1Fdu2bUNRURHKy8sxduzYgOdGjRqFHTt2RHq5RJRwMkL5LXGUTrSyyIaxX1bivo21GLuyEk7y9hIE0Q754zZrwN9/3uXxbnL20g+CTGXHJWrCNDMzE5dffjnmzZuHkpISuFwu/Pvf/8bOnTtRXl6O8vJyAEBubm7A63Jzc1FRURGNJRNRgFWBH0+h/N9urPEZWEudE6tP2eVfQBAEEYccsga2hdpf7QCgbSif6BhEdfLTP//5T0ydOhVDhgyByWTC0KFDMXnyZOzdu9e3jXh0mSAIsuPMLBaLqjWo3T4axMMaAX3WWesAgMApISfPlMBiC81tGunv0u4KXPt6SzkGOxxcr+3Iv7vW8K7RbDbrvBKC6BgYvW6vEF2mJEw7LlEVpv369cPq1avR1NSEhoYG5OfnY8qUKejTpw/y8vIAABUVFejZs6fvNVVVVUFeVH/UXFgsFkvMX4hiYY2nGp2otrsxtEsijBJGRq91ljW7gB1lAY916ZYPc3++kXb+ROW73Fwc8Gd+184wm7MUXxYLvzsP8bDOeFgjQbQ3fLo0xNeTLu24xEQf0/T0dOTn58NqtWLdunW44YYbfOJ0w4YNvu3sdju2bduGESNGRHG1HYuvTtlw2fJyjFlZif+3vibi78/KyWyJ4wT4FFOoZpogCCJ+MIWaXHoWVqtAomMQVY/punXr4Ha7YTabceLECTzzzDMwm824++67YTAYUFhYiPnz58NsNmPgwIGYN28e0tPTMXny5Gguu0NR+F2tr9ho9Sk7fqxqxcVdkyL2/k6GbWqNs6p8f1ISSJgSBNH+MZ11e0Uix1SrK4LNKeCNA42wOQU8fH46OqeYNNozoYaoCtP6+no8++yzKCkpQadOnXDLLbdg5syZSExMBAA89thjsNlsmD59OqxWK4YPH47ly5cjMzMzmsvuUFhbA0/5HRWRFaYs42SPE2HKuuNPMpIwJQii/RNucCgaOaaPbanFp8dtAICNpXZ8c1O3yC+CiG4ov6CgAD/++CMqKipw+PBhvPzyy8jOzvY9bzAYMGPGDBw+fBjl5eVYvXo1hgwZEsUVE5E2FqxQfqx4TK0tbtS1SucVNDqC10njVIlIQxP2iGjgDeWH3C5Kw7Xw4hWlALCr0uGpcSAiTkzkmBLxQ6RlFSuUHws5pkstTTj3k1IM+KgUS440MbdpYAhTGR1LELpAE/aIaOANDinpUptTgIPhgFAVyj+7rd0poJk1LjBEvNE5QRDQ6HBT3muEIGFKqCLSJyaz+CnKHlNBEDB1sxWtbo9wnrbVCoHxvdQzVKgjRry9RMeAJuwR0YIna+n53fXo/kEJLvy0DLtFk6LUXmveP9yEnh+W4NyPy/Dyj/VwaRDe+9U31aixu3Dr/6rR88NSjF9VidpY8Iy0c0iYEqqI9A0jS8dFO5QvnjzlFtij8lgjSONpOAAR/9CEPSJamBQ8pqcbnZi3z+OVL7O58dSOwMlRakylSwBmfV8HpwA0OgW8sKcBk/5XhZKm8ELxR+qc+O2mWmwsbQEAfF/pwL8OsSNkhHZEtfiJiD8ifa/Iisq0RFncsQQnSytTKJ+INv4T9gYPHoy8vDwsW7YMO3fuRP/+/WUn7JWWlkrulwaZRI/YXWdgb2mXwwGLxYLGpiSIpYbFYsHy0gQAbYW0uyodAZ/tjN0AIJXrnYtKy2BtTQ54bHNZK65YXoq/ntuCKztLG17ve3qcLsH9sdcVtwT8/fwP9ZiUVha0nd7E7u/ehlaDTEiYEqqQ0oQfnEnAO9tLcE66Ce+N6YzBnRI1eT9WsVBrlPPRWYLTs85A30BDa/B2FMonIo0eE/ZokEl0iLV1fnHShj1VrSjolwqgMuC5lOQkmM29kHm6GqgOHMVsNpvRzdUEHLMGPe7FVO8Evi/nWkfX3G7A0bqgx+ucBvzh5xQUDknH7MuykSDKL/D/Pl1uAdhSwvV+kf4NYu13Z6HlGimUT6iCJUwrbC78/WQSGp0CDtc5MefHes3ej138FF1xx8odZXl26ymUT8QA3gl7xcXFOHDgANavXw+HwxE0Yc8fpQl7BPHFSRvu2VCDBfsbMe7LyqDnw+2MpybH1KUQiVr8cxNe3itfzEc+g9iBhCmhCta5+9kJW8Dfn5+0M7YKDVYCe7RzTFkeU5YRtTNUNSsXlSAiAU3YI7Tk/o1tkwBZKUredlGhClQ19/DitzcagvuorhBdp8Q4qeI+ZqBQPqEKlgDT83RmekyjLEyZHlNWkRZLVJPHlIgwNGGP0AOlm2yvpTNIlD8pWUI19/DiS0LnZCOWju2MiV9V+QTukTonKm0u5Kaypzmp6TLldAtBaQGEdpAwJVTB0lV6ai128ZN+78cDO8c0eDuW4Y62t5foeNCEPSIasFr9eamyu/DHbVbJ5wGVHlPRxiYDMCIvGUO7JGJPlcP3+NbyVkzqyy6oUmOaK+1udE+jcaV6QcKUUAVLE+rqMY3BPqasqnzedVIon4g0BQUFKCgokHzeO2FvxowZEVwV0d7x3oSz/IpvH1RuuRRuKB8ARuUlBwjTLWUtksJUTkiLKW92kTDVEcoxJVTB9pjqJxRZIfJwe9OFC7/HlEL5BEF0TBhm0secH5WniqkqfhJtajwrh0fnJwU8vrU8sIm/P6xrjRSNajYmVEPClFAFa8KRnqcoyzgdq3dG1WvawMgxZVWFsgoCqI8pQRCxTmmzC3P21GPJkSa43AKWWpowZ0+9KqeAtzWeTNcxWVQ12BdtbDyrbK7IC+xteqDGAatELpgajymlZOkLhfIJSbhFaIRzTF2CR5wO4eiVWmlzYdb39ahvdeOuzgaE02Wt2enGCz80YNGBxuB1MvutUh9TgiDiC5dbwC++rMSZsyL0zzvrfFGiJUeasO/2fK7Cn3DTltRYymCPqYdOyUYM6ZSAn2udvn1uK2/BxN7B4Xw1pjna6WTtHfKYEpKwbiBZQjHSVfkAcKjWwX5CxPTtdfjoaDO+PGXHHw8mh5V2sHB/I1OUAiqKnyiUTxBEDLOhpMUnSoHA1KWSZjdWn+JrB+hNZQq1dj2cHFP/VlGj8wO9plvK2OF8pV6o/ojHUhPaQsKUkIQ5p55hLfStymfvvMLOZ0U+O9nWu668xYh91XyClsVcmbwo1jpZ3xUVPxEEEcuU2+RVV0kznyprdkYxlO/3pqNF4fyt5YEjRr2o6WMa7SEv7R0K5ROSsE7USHtMpSImoUZSbDqFYFh2ivqYEgQRb6SKO9OL4PVmOQUg591iVe+d824xru6ejCVjOquKbomvS/4fYZSoAGpvtQMNDjcyEwM/iZo+phTK1xfymBKS8FaaR8NjGmpIXq8GAqyUAwfDseDNO91b3Yont1vx5s+NqpLuCYIg9CQlQV6YhuoB5WVTaQs+tDSparDvEMQe07Z/d0s1wZzd5oNzCcDOiuBwvhqPKRU/6QsJU0IS3krzaHhMQ9Vyeq2VOTpVIpT/wZEmXLeyEm8ebMKTO+rw2k/svFWCIIhIk6LgMY3EvKOZu+pV2Xixt1NcmzUqT9Q2qiw4nK8qx5RSsnSFhCkhiYsZymdU6ke4jynAJ0wj2dqK6TFlLPJYvROPbLEGbP/O4SZdv0OCIIjaFjde3dfg8UYKAj47YcO8vQ040+gM2E7JEuntMfWiKsdUXJUvWiRPARR5TGMHyjElJGHl3LCKd3T1mEqG8jley9hGan/hwi4U49vudKMLR+udMGcrt78iCIJQiyAIuHF1JX62ekToX3bVo+as2+8fPzfiwK/ykXzWU6qUa2mMiM9UnTAVO0zETl+xx3R3VStsIm8CtYuKHchjSkjCW5UfjXZRLG+uGNZa9WrzwQzlqzBe64vZlaIEQRDh8kOVwydKAfhEKQBU2d345Giz728l2xopj6mg4soidpiIQ/k9MxLQM90UsP3JhkBPsZriJypi1RcSpoQk7Kr8WCl+Un5tK0OE6tXmg7dQTIr1xXy9AQmCINRSodAC6oSfSFMj0PRElcdUkPeYAkBOcqDcEYtLHmeHF+pjqi8kTAlJWMngkQ7lS+aYcryWOas+xBCMUhcA5uQniUWem52Aj67rHPDYd2WtFB4iCEIXxDmXYvyfVRKEHEOfNCG84qfgRYq6QwVd31R5TMlW6woJU0IS1h0ks8E+44QOZ8JSwBqkPKYcRoQlDEMVf0p3yMx8XMZ7XZWfhK9vzMXEXinontZ2+jU7BWwvZ08kIQiCCAclMen/vJoiID0JJ8eU9XnFXbDEjgs1n9tOwlRXSJgSkrC8leyCqOANv69sxcoiG+xnd3Kw1oHPT9pQL+VGlECyXRSHn5bpMVUZpmp1CfiyyIaNpfKhdp7ip9mXZeGzCV2Rk2yEwWDAmB4pAc9TOJ8gCD1QEqYGP5+pkuaKmMdUwxxTAEgQPSi+vqlqsE85prpCVfmEJOxQPiPvlHGOjl9VBQC4NDcRT1+ShV99Uw2nAPTLNGF7QZ6vAlQJKWPBl2PKKn5SZ1DuXFuN9SXKhUk8fUxv6ZMKk59xvO6cZHzkV3SwvqQFz6paHUEQHZHyZhc+PtoMa6sbE3ql4ArR2E0xih4of4+pgnGNkC5V2S5KnGMavEqxx1T8OdVcGlj1C4R2kMeUkIQ3b1KuyOf7Sgdu+7raJ15PNLjw6bFmye151gDwGRHWWtXkBp1qdHKJUqn1iL+XJJEYH9MjOcDI769xKBYpEATRsREEAZP+V4W/7q7Hq/sbcdNXVdhZIW+n1OSYKpnIWBSmPB7TRNGD4teoaSVI9QD6QsKUkIR1nrLuptWeo9sZ4+CkkJrGwWNDWIJZzcSO0iZ+kcjyGovzUpNEZ1vnFBMu6RrYu5TaRhEEIUdRowuH/Fo/uQTgmzNKwlR+n/66VWkCkiFC/aK07GMKAAki+yt2ekgV2rKgdlH6QsKUkIQVRq+yBz+opi0SACSqsGtSHlOe4irWXa2aO101n4pVKCb+XsR37AAwlvJMCYJQgbgxPKBcjKNY/OT3b6UioEjlmKqxv+KvhCVsxDmmYo+pUgqDP+Qx1RcSpoQkLLFVZXfjiNUR8JjavneJnPmlcvsONZSvxqA0q7iFZnkZxHfVSSxhek5gbtiGkhbNOhoQBNH+YOmGFLOxAAAgAElEQVSncFs8BXhMYyaUz28Hg6ryGR9YnGMa1C4qzBzT3ZWtuGRZGfp9VIKllib+nRFBkDAlJJE6Uf8tyhFVc6cJsAWaFFJCksdmMUP5eglT0aaCIAQZL3EfPQC4rFsSMv1cyJV2Nyx1zuANCYIgwHYYqGkOz8LfIsdj8ROPxzQ4x1RU/BRmVf7MXXU40eBCbYuAR7dY0RwrkwrikKgJU5fLheeffx4XXXQR8vLycNFFF+H555+H09l2URYEAS+++CIGDRqE/Px83HjjjTh48GC0ltzhkDpRq0XhfDV3mkBwrqUcx+rZIo3LY8rYSE1ukBphyqrw9H/EZEBARb6XRKMBl+YGznH2zx8jCILwh2XClOyhktlT02A/UiNJw2mwzwrKmYJyTMV/878h69qyza8PtUvwjIElQiNqwvTVV1/F22+/jblz52Lnzp2YM2cO3nrrLbzyyiu+bRYuXIhFixZh7ty5WL9+PXJzc1FQUICGhoZoLbtDIXUX7gjq/6Yyx5QzlC8IAg5LiDSeMA87lM/11gCAsmb+jV0C0Ox0w3q2uoonjO9lUE5g17aDtWTQCIJgwzK3/zttxxsHGiW7eiiH+j32qbTZhdd+apTflmuV4aPG4bG/JtBm8lXlh94uinJM9SVqwnTnzp24/vrrMXHiRPTp0wc33HADJk6ciN27dwPwiJLFixdj2rRpmDRpEoYMGYLFixejsbERy5Yti9ayOxRS5574hGaNKZWDN5R/psmFRgnrFPJIUk4R/eeddfjL9/Vc2wLAV6ftMH9chv4fl2L+3obgML5J+rWDOwVW5kuJcYIgCJZdPtXowtM763DtFxVMbx6PF7TVJWDcykpUMgpco8HMnXUhv5bVx1RcdBs8klRN8VMoqyJ4iZowHTlyJDZv3owjR44AAA4dOoTvvvsOv/jFLwAARUVFKC8vx9ixY32vSU1NxahRo7Bjx46orLmjIXWiisMmavObeEP5crmWoYbyee50TzY4seiAvNdAzKbSFjQ5BbgFYPYP9SgReVsTZeJf52UHekwPWcljSmgDpUy1P+SiRSXNbvzneHCfaEGhxt0IYGt5C4o5okSR8hVWq+ntJ4Jn8lM4HlOekaSRysVtj0Rt8tO0adPQ2NiIESNGwGQywel04oknnsADDzwAACgvLwcA5ObmBrwuNzcXpaWlkvu1WCyq1qF2+2gQrTWeqTYBCJ4oUlvfAIul2vd3XWMyABmXoAhrdRUsljLF7SxV7PcHAGtdPSyWKtnXnykLfn1tfRMslhrZ1/23NAFAkuw2Sny450zAPhLhlPwdE50AkOb721LngNMdH8cmEB/r5F2j2WzWeSWRxZsytXjxYgwZMgQHDhxAYWEhkpKS8OSTTwJoS5latGgRzGYzXnrpJRQUFGDXrl3IzMyM8icgxChpoiOMiAuPx7SsmU8IxkMQm6+PqehvFTqYR5gSoRM1Ybp8+XJ88sknePvttzFo0CDs378fTz31FHr37o177rnHt524ma8gCLINftVcWCwWS8xfiKK5xp8TbcDBYBGXnJYBs7mL7++ko5UA+Jvmd8/LhdmcobjdPlMzcKiW+VxGZibM5s6yr+/kbASOBoaDElLTYDb3ln1dnqsJOGZVXJ8claZsADbf31kpSTCbe0lu331fKUrPXhicggGn7QaMHzowrDVEAjqHYhv/lCkA6NOnj2zKFAAsXrwYZrMZy5Ytw5QpU6K2doKNkibyf3pPVSse3FSr2OnDACA3lS+UFQ+95VkSIUH0oDgiqKb4qdkpKGoRInSiFsr/y1/+gt///vf45S9/ifPPPx933nknpk6digULFgAA8vLyAAAVFRUBr6uqqgryohL6IB3KD31iBqBs2ARBwJPbrbh/I1uU8uwDCL2PqRa25oCogClN3ERPxHk5gXmmJ5rlT82fahwYsbwcfZaW4O2D6tIOiI4DpUy1PxTtp9+//7Krjqv9nIFjv6z9xyrMHFORSQ1usM+/fwGUZ6onUfOYNjc3w2QKDP+aTCa43Z6jo0+fPsjLy8OGDRswbNgwAIDdbse2bdvw3HPPRXy9HRHp4qfAv9VW5Stpw12VrXjzoHyDYq6RpKx2UREKwfxcG3gxSFUQpoNyEvBtSdtYwePN8tv/bU89Dp+94Dy9sw6T+6chJ5naEhOBUMoUP/GwRgA4feYMgBTJ52tqa2GxeBw635WlSW7nT1VVFc40u2X366W0tAwWtwv+6UexRmNDcKpXnTURQJsDoLyqCujd9rtXVgc+r8RPR44iO2DzwO+j+MwZWBq0KySLh+NTq5SpqAnT66+/Hq+++ir69OmDQYMGYd++fVi0aBHuvPNOAJ4QfmFhIebPnw+z2YyBAwdi3rx5SE9Px+TJk6O17A6FlOAMtypfqdXTvL3K7cD4PKbqR/cB+ozcS1cUpoEG8biCx3T1qbbRpa1u4NuSFtzaLzX0BRLtEkqZ4iMe1gh41pnf4xzgQLXkNjk5nWA2Z3v+2FzMtd/c3K7Iy0oAfpbPvweAbxoy0aN7OgDpiFa06ZSdBbO5U8BjeU31wOm2a0tWpy4AymE2m7GlrAWfllVDjT+4e5/+OCfdz7km+q579uwJcz67RkIt8XB8arnGqAnTl156CS+88AIef/xxVFVVIS8vD/fee68vIR8AHnvsMdhsNkyfPh1WqxXDhw/H8uXLKSE/QkhpOHHIQ+tQPo8u5OkEEG4fUy1RCuWLe5kqhfLFiJtHEwQQmDIFAOeffz5Onz6NBQsW4J577glImerZs6fvdZQyFbsoh/LVR4WMBgN3KH9LWSu2lPHXFEQDZlW+6EbLdfYDryqy4e71yoJcjM3phpqiX4KfqAnTzMxMzJkzB3PmzJHcxmAwYMaMGZgxY0YEV0Z4kQzli0Sh2lC+0ubith6h7ANgh/K1avNxQedE/FTD39ZJOZQf6DEtshngdAtc3wXAHndKEJQy1f5QuikPpTjJaIiPoiZe1OSYPrgpNM+vmsmAhDqiJkyJ2Edy8lMYSeOe/co/L57QwYLnLUMN5fOQoSA0xaSLe5WIyEk2Ij/ViDKb55M5BANONDhhzubLeeL5zoiOB6VMtT8Uq/JDNHHtSWaxslDEI6G9VfhNIQpMm8LrqGA/dEiYEpJICU6xh5Q1YUkOpRxTHu8fV4N9xvrtGt3lpovHiCig5DEFPJX5Zba2AqiDtWxhyvq+yWFKsKCUqfhnR3kLHtlihd0l4LFeJuTmy28fioUTBL4xz/ECq4+p2GSrdaiIsSlchP6w1YpquxtXdU/G30fnoNkp4HebavFTrQP3npuOpy/JpHZTEpAwJSThHUmq1gmptD1P+FrgyTENMZTPY7CUipnEKOWYAsB5OQnYWNomTA9bHQCCC5qaHMGfQW0BGtExoJSp+OepnXU4crYDx5xjiZjTTd6GhaIv3WhfoXyeyU9qU9DEKIXyD50ddLD8hA3X9kjG6UYX1p/tvPLy3gbc2jcV53fm7wLQkSBhSmDNaRveO9yMIZ0SMH1ols+755Ksyg/8W3WOqcLz2nlMgzdyCmDmbjrcAhbsa8Ar+xpg5yiQSleZ1MkjZAeL8kwPiSa4uNwC3jjQiP+esEGM+LN6P8/uKgd+1T8Vv+wfu61dCIKQZk9VWy57ZasRx+vl+5KGIrfcgqDawRDLsHJMxdlUjPt7VSiF8v15dEvwwJaX9zbgvTHyQ2I6KiRMOzglTS7ctbYGAoA1p4HMRCP+cJEnhCddlR9uuyj557lyTHmKnyTWZXcJyBC9x1JLM/62R7lNlRe1OaZ8ofzA0/GQNbC46osiG575vp75WrGX1//z/O+0HUM6JWJwJ7o7J4hYYF91K36ocmBMj2T0yQy+DJ9pdGJtcQsuYnjUrK0KHtMQ1vN9ZSsm9mo/7eZYbgPxdUVvjykROiRMOzifnbQFGLJnd9f7hKlU7miQx1Rl7Egpl4mVHxS8D+VtpJrp210CMkT2ftpWdSNI1eaY8oTyxS2jjtY7A7y7cpOwxL+V+PN8dLQZsy/L5l0uQRA6sauiFRNXV8IpAFmJBuy4LQ/d09o6J9S2uHH1F5WoaXEzO4RYWcnzfoQSyv/8pB3J7aiAktU+T2yCw84xDVOYtp9vW3uoZqKDwxrR6c3flAp1iEWQ2hNceSSp8j54+phKCWstCqD0yDHtnGJCN7951S0u4GRDW9hO7ntjpS34U9tCSagEEQv8cZvV1/u53iFgvmigyMdHm1Fz9nxlndVK53IofUwB4NPjwSlC8YqRIfvEHlO1Rbti/IufeGoexFDdkzTkMe3g5KcFNwiusLmRl2aSDHUEN9jXdiSpksgC2CJNEAR8YGnGTzUO3G1OY1blA8D/fVuDqednoKbFDUudE/93XjrHqgNRm2PKI0wBTz/TCr/K/ENWJwZytIzy/01Yd/L9s+hUJ4hYYL+o/7H/KGLAE1aXw6okTCnCLNEuKvDvcP0T/qF8+sq1ha5WHRyW5/GQ1Ym8NJNkjqa4wb7WOaY8+2PtYsmRZjx2NoT9/pEmDGDkbgHA95UOTPm2LSz+4ZFm5TcUkaE6lM8nZM/LScCm0kBhelMf5df53/1b6oIb/6tcLkEQEULs4fSPmrBQEqYUG5FoFyUWpuF6TP2EaSi7IpMsDYXyOzguhhUrbvKEj3lyTN2CoPqkVArD84RYWOt+zC+vssUF/GyVr1710hjCrbPa4idej6m4Mv+wlW+6lL93uLgpuK2AQloaQRBRQmzu8lLlx1zqkWPa3uAZSRpujmm4HlMK5UujSpjW19dj3rx5uOWWWzBq1Ch8//33AICamhosXLgQx44d02WRhH6wRKD3fJM6cd1+zZhDObm18Ji6oxw86apw8RDDK0zFlfkHOcW1/90/6/sLN5+KiBxkZzsW4jOzU7L8Zbm2Rfuq/PYG30jS8L4p/9eTx1RbuEP5JSUluOGGG1BcXIwBAwbgyJEjaGpqAgB07twZS5YsQUlJCebOnavbYgntYYkYr9iUO3EdbiDZpD6/FFA+iUPNMY0USUYgO0kfj6m4Mt9S54DLLQSN0xOzs6ItL40VogrXO0BEBrKzHQ+1tkxp4hAJU7bHNHgkaXjv4f8z0H2/tnB7TP/617+ivr4eGzduxKpVq4Kq0G688UZs3LhR8wUS+sIWMWe9oTInm1e0hjJxSNFjytHpOZrNoFNMBmTpVPzUJcWE3BRxZb5yx/8vT9nxw9miCVY3BR6xT0QfsrMdD/GZGW4onkL5vCNJw/ui3AHCNISq/LDevX3DfXVdu3YtHnroIQwZMoQ537Vv374oKSnRdHGE/rDEp/cxOY+p1wMnNR1KDsUcU45dRlNnpSQYkKnSY8rTYN+LOTvQa3qy0cls6yXmz7vqALB/NwrlxwdkZzseYnMYbpqS99WhtDBqL7DyN4NGkobtMfUL5YeyA1KmknAL0+bmZuTl5ck+73ZTvDDeYOaYej2mMj+nnh5Tqcb4gfuIntFNNRmQqcNIUi/ZSYH7tjkFNHB80dvKPR5TVmEYhfLjA7KzHQ+xKQvfY+rZQUe+F+UaSRrmaeT/O4Xym5EulYb76jpgwADs3r1b8vm1a9diyJAhmiyKiBzMHFMOj6n3daHcdSrpTh7vXjSNbnqCAck846nOkmwKzm+SQ+xdtbsENCiMIfSH9f1RKD8+IDvbfnltfwOG/qcs6HFxu6hwz1SvSe/IZzzL3Iob7IcS7fNne0Urrvq8AqM+K8eDm6Sn8klBwlQabmF677334pNPPsEnn3ziu2M3GAxoamrCM888g02bNuH+++/XbaGEPsjlmMrdUbZ5TEMpflJqF8WzD9VvqxlqwvKAx8OqBrHotbsE1Ku4vWdX5ataAhElyM62T47VOfHM9/UoagzOFxfbsrBtm6DRfuIYlskVm+1wbeKpRhf21zjwc60Ta07bVb+elapDeOCuyn/wwQdx8OBBFBYWIjPTM0v9vvvug9VqhcvlwkMPPYQ77rhDt4US+iCXYyqXHN6WY6r+PbWoyo9m8VOSSqGZztlc34tYyNqdAupVeEzlbjaI2IbsbPtkwf4GyeeCip/CfC9B9P+OCI/HNNp59yRLpVE1+WnBggW48847sWLFChw/fhxutxv9+vXDbbfdhiuuuEKvNRI6IpdjKusx9fYxDaVdlOKaOPYRxRxTNWF8QL2HNVnUItXuElBt578DYN1skMc0fiA72/6Qu5EWm+BwTZt3f/VxPFUj0RiezWLlmIpHkkbTuUHIwyVMbTYbnnjiCYwfPx6TJk3CiBEj9F4XoSOHrQ6kJhjQOyOBWRTD28fU//9qUPKycuWYqn9bzVDofx0Eb6soL8E5pkCVXblllLcei3JM4xOys+0XuRRz8ZkZrm0T4MmfHLOyMsw9RY+Ke3qg03uhd5+IC48puUwl4brEpqam4rPPPkNdXZ3e6yF05qkdVoxYUYFhy8rx8dFmdtiXY6pTW+V+KB5ThRxTZQ2m290uTz6o2MApka5yUD0rx7SSw2PqHbnHHppAwjTWITvbfpG70AZX5YfZLkrw9DU+wxhNHC+Em3/JHkka+He0O5WQLpWG2/czbNgw7N+/X8+1EDpjbXHjHz97psg4BaDwu1pmz1Cfx1TGQHrvNkOqylcwCDzePfHStBJe3VKVTwm1OaZqi59YOaY8oXyvx5TVJ5ZC+fEB2dn2iRqPadihfAj4sapVecN2DMuKB3lMo9znlYSpNNzCdM6cOVi5ciXeeusttLZ27IM+XjnNuINm55h6n5Pel69dVEgeU3n42kUFbsPTgJ6H/DST4jbeNqOLr+rEtU+VtU9BHtMWl4AqDmFq8oXyg5+jUH58QHY2vnj/cBMuW16OO9ZWo8IWbF83l7Xg6s8r8P6RZsl9iG1ZuGeqVp7ArikqDddZTDFQdsVqzxeUYxptjykpU0m4i5/uu+8+CIKAP/3pT/jzn/+M/Px8pKamBmxjMBiwfft2zRdJaAPLcVfXEnx2toXy5aryQ2+wr9zHVP0+lPL8MxIMmJzfivfOJMpu15kjgdTrMb1jQCoaHW68e6gJ1lY3SprZiyhWGVJj9THlyTFtC+VL32wQsQ3Z2fjhTKMTf9hmhVsALHVOLNjXgBdH5PieFwQBhd/V4jSjRZQ/WlflOwVtKvILh2Rgwb4GNKoMixkMiHpLAKbH1BBbOaaENNzCtGvXrsjNzYXZbNZzPYSOsLyKpQwxpab4SWm8KAulHCoe7554E7uC2u2cYsTUvg58UpoIOY3HU0GffPZu3Ggw4LeDM/DbwRkAgJk76/D6gcag7Vmeatn9M3JMeTym3lA+S4SSEY4PyM7GD68faAywQ4t/bgoQphU2t6IoBRgjScMN5buFsNMBAPn0AzlMhvDHfYYLs4+pSK161xgtHU0OU2m4hemqVav0XAcRAVjirZwRfvJ6TPka7Ktfh5yGFAQhpAb7SqH8pLNWNtlkUBSxSiRKRPuTJB7vk6GqKxtSGMKUJ8fUOwua5emmHNP4gOxsdDjRbMCBEzZc0yMZ+2scqG914/peKUHz1f1RioSIQ8dSxKrH1GgILdwcWgKAthhZ7aJED7kFz3/REtKhCNONJS1odroxvmeKqmmC8Ya6KyYR19gZZ1+5TdpjKhfKDyvHVOYlvAZC7KltVRSmnv+nmgyoUzDbN/VOwZenPJM88lONKBN9R0kSBuHCzuw0gT9cmCH7fmKChKlTQBPHF+N9GaugjTymBMFma1kL7t6TAodQE/D4jb1TsPS6LpKvq2TYTn94BZrWk5+06lgSqsCMBb3EWoPBYAjqj+oSPJ5UZxQaGKgV/XP21GPOj55BDbf3T8Vb13TWYVWxAbcw3bJlC9d2o0ePDnkxhL7wegp58kf1yjFVEphexG/LSJUNINHU5jFVYu7IHCQa62BtdePPw7Iw7svAfoBSVfmT+qbiTxc78WWRDQdqnQCAJy/OxC19U5nbSyEWpjaXABuHMPVuwhKh5DGND7S2sxdeeCFOnz4d9Pj48ePx6aefQhAEzJkzB++//z6sViuGDx+OefPmYfDgwarWHc88usUKhxB8Tq86ZcchqwODctg3nKxokz+8+jC4Kj88Zel0C5p4TEMtzokNjyn78QSDAQ6/b8cpeJvxR/7G3aDSZ+oVpQDwn+M2zB3hQucU5WLdeIRbmN50001cvcVqamoUtyGiA7cwlRE4XrxeuVByTDeVtmDwv0vx8sgc3NQnULTxCijv27rcAp7YbsW7h6WrXgE/jylHDuk56Sa8O0b6blTKY2o0GDDjkizMuCRL8T3kENuaBgffheZ0owu3f13FFP6R8pj+61Aj/vZDA/LTjPjXtZ0lL+oEG63t7IYNG+BytQmosrIyXHvttbj11lsBAAsXLsSiRYuwaNEimM1mvPTSSygoKMCuXbt8I1HbO0frnZLPbS5tkTyGlTymvKec1lX5biH8llOAx56Fok1jwWMq5X9INAL+9xNOgT/lQmvCrcqvbRHQOUWbtcQa3MJ05cqVQY+5XC4UFRXh3XffhcFgwKxZszRdHKEtHIXdADxiD5Cv5A4nxxTwFF09vs2Kib0Cc2V4BZRXfG0qbVEUpUBbDzsljymPcNX7JjVFtAarkjvYj2+KW5iPR0KYWlvceGpHHRxuoLrFjTl7GvCejMAngtHaznbt2jXg7w8++ACZmZm49dZbIQgCFi9ejGnTpmHSpEkAgMWLF8NsNmPZsmWYMmVKeB+mHfBTjQNuQcDLexvw+QkbRuYl4/nLs5CWYAyqVhebDt5TTrxZuKeqU9DGY6qy/bKPWBCmrBxTgD2WNNTPqSWnGp14dIsVP9U4MDgnAa9d2Ql9MxNQ1+rGk9ut2F0arEDbc7spbmF65ZVXSj539913Y/z48di6dSuuueYarv1RiCnysHJMWfjGjcrcdvPkoSpRbnOjuNmF3n7FQVJtn3qmmwImmXi9DH/5vp7rvTLOTl9KVxCevz8/OB/0/5nT8KHFI34TDMCdA9O43jNUxKF8qwYzryMRyl91yhbwPp+dtOn/pu0Mre2sP4Ig4IMPPsAdd9yBtLQ0nDx5EuXl5Rg7dqxvm9TUVIwaNQo7duwgYQrgp1oHtpe34sU9njDqz1YnLu6aiHvOTQ/aVjycgzuUL9owXFHpEgAtpGnoVfkCol1zLu0xDQzbRzeU38bMnXX4tsTjVPiurBVPbrfi0190xVsHm/DvYzbERoJE5NCk+MlkMmHy5Ml47bXXMGPGDK7XUIgp8qgP5UtvE87kJ3/Eupbl2Ruck4C/XZ6Ngq+rfY+5BOntWXQ56+bMTJI+wa/tkYzBnYLDdk9enImTDU6canThiaGZ6Kqzy1Ts1a1rDd9oRsJj2o5v4GOCUOysPxs2bEBRURF+85vfAADKy8sBALm5uQHb5ebmorS0VHZfFotF1Xur3T6ySN9oljW04KnN5QDazvlHt1hxhaEk6HVpcAZ8zooWAwDl/HKXIAS8rro6EUDoKTBNthbU1jaHtQ8AqKqshMudCLVndrgSamiWCxaLBb/MT8R/y0L7DGWlxbCwUi1cKfBfoVMwwOl0IRrWq67OCovFU7+w5lRqwBq+LbbDYrHg+R+kj82TJ0/ClRpbRa2857lSOzzNqvLtdruq/FIKMUUeNcVPbkGQDSm1VeWHtyaxt0AsoAZkmbCtIC/ocYcbGLuyAoes0vlh/ninmGTJzK1/6mL2DU/vjAR8OTGX+ZweqB1hykMkPKZS4TNCO9TaWX/ef/99DBs2DBdddFHA4+KcVkEQFPNc1fRZtVgssd2XdXOx5FMJiYlnGwQ7Ah4fOHAgsLkk4LGM1GSYzb18f6c2OoFd5RwLMAR8PzkN9cDpBpnt5UlITEJ2TjJQ0hTyPgAgv1s3mE7XqS7zDzeUP//qfJhzkzCruxP//Q/P9xfMuX16wtwtOejxlB/LgNY2h5hTAAwmY1T6ReXk5MBs9vS9bRUdgyaj0XNMyBybffv2Rf+s2GmspOV5HrZ/uL6+HqtWrcJrr72GSy65JKR9iENMRUVFsiEmIjR4halLUBYyTl8eangndHDbp8DnvYVGrAP1hyoH41E2uWeFaWai9CHPU7EfCfRYRyQ8prGQW9ZeCdfOVlZWYvXq1bj33nt9j+Xl5QEAKioqAratqqoK8qJ2VAQhOLUGAFoY+fpiLc97Lxh0amrQLiqaDfbDNQOX5iYB8DgEXrkiR2FrNqkSc6DFmVwuQTqnt0eavuFz71JYXRgykzq2MeWW2506dZK8ixYEAb1798a8efNCWgSFmOTRao2llXwhosZmGw5ZjkI2xFVZBYulDKUVCQCSQl7TsZNFENLaTszjjYHhL5ejFRaL5ayhDT2301lXCaQAzkYrpL6DsjOnYKmNfmjE44XWNo/V4RZ0O9a9+62oNAFIZj4XbbQKMemNXnZ26dKlSE5Oxm233eZ7rE+fPsjLy8OGDRswbNgwAB6P7LZt2/Dcc8+F9gHaGW4AyYzMHdZNfqj9SIOKn8JUploVPxkMwIjcJMmCykgQqjhOk7i5TxTt0OmWFvFSbQG1hjXyVTw+lUV7lq7cwvTJJ58MMpgGgwE5OTno378/xo4dC5MptNw7CjFJo+UaU6utQLFyeMeUlII+/XoC26RvArI6dYHZnIVO9gbgBF8BEovuPXvD3KVN2ForWoEf2/qGZvqFxwxbikM2uOf36QHYT6N3XhegmL1ec78+GJgdG+2NkrefQYtb+lhPNAJ/uzwbs3+oRz1HDqoAA/oPGKj5tBD/43OfqRk4XBvwfCycX/FwnnvRw84KgoAlS5bgtttuC8jPNxgMKCwsxPz582E2mzFw4EDMmzcP6enpmDx5siafJ96R8pjyCNNQvZbheju1arBvMgCzL8/GNysqlDf2Q0sLE+q+pLqrsKrypW4gpNoCaoV373WMriva3FrEL9zCNJRkex68ISZ/L4B/iKlnz56+xynEFB78xU+CYujXm1sabu6i+EECK+EAACAASURBVPXi9/W/aw1ndFzXFCNgBzJlckwjdYfMQ7pJemjAdeck4z+/6AKjwYC5PzaAN/bX6gZSdYxOsb49nptJog097Ox3332H48eP46233gp67rHHHoPNZsP06dN93U+WL19OBaZnEQR2ag3LlmrV9ilcSeJ0azWS1IBBOYn4jTkNH1iUW/JpgfibDtV0pEkIU7HHdMahZKbH0rNtaO/Ni/ezWRmOhY4+qI/7q7/55puxceNGyec3bdqEm2++WfUClEJMXrwhphEjRqh+D8IDd46pW1lwtlXlh3cGtQYVNUn3BgznBraLL8dUeifJMZQkmW6S/l5TTAZfoZGaHF+980xZhwxNnFKHHnb26quv9olOMQaDATNmzMDhw4dRXl6O1atXY8iQIarX3V4RIAT1FQaAFqbHVNwoP7TzLdzT1C0ImihTrzn05nxGArEQDdUkS3lMxWLzlF1aAuntqPDundUOsKObTW5hunnz5qAkeX+qqqq4x+l5UQoxvfrqq/jiiy/w888/4+GHH6YQU5ho6TH19jgNV3iIi53E56i/cQhHN3p7DGbJtIuKleInwOMxlcLf6LLutqVQmlQTLqxjoaWj3/qrRA87S4SOW43HNMQc06D9hPYyH05tdKlPHKj1WoYTIAnymIa4Hykzn6BicXo7KrxLYYbyOX7A9hyI0qzXQHFxMdLTg5sOy0EhpsjCO/nJ4VZuA+UVIa4whUdwG6jAv/1DL+E0Qk5LUK7KT4qhscPpCdKfM1QBfcjqwIBs/dqLsG5mWl1CuO0UCT9CsbNE6LikckwZ4d/gIiZ+3ILgi4KEPflJs1C+5//R1D+htqCTSh+SKNZnonco3ys+mR5Tjh9QTrwer3fCUufE6PwkZOj9QXRA9iq1atUqrF692vf3e++9h2+//TZoO6vVio0bNzJDRXJ4Q0wsvCEmvXJbOyK8k59cbvmpT0BbCDlcj6k4JCben/85pcUNbJZMG45YCuVnyITyQ+1zerjOiRv9/t5Z0YLPTtowolsyJvVVbgTuz97qVvz7WDPyHCY8OtCTR8oSpqy2OkQgettZInScgiBRlR/8WKhV+YBHTHpvjMMtfHEJgqbtotSaxXCsqPi1WpvkBBU71DuU7/2JWANUeDozSB1fW8tacNvXVbC7gIFZCdg8qRszHSWWkRWmBw8exH//+18AHqG4a9cu7N69O2Abg8GAtLQ0jBw5EnPmzNFvpUTYsPKiWHhC+fLb+Brsh2kBxe/TKlqjf2VkqKGLPL+KH6ncIwCaV6yHg1woP2SPaW1b39eiBieuX10FtwC8caAJS8d2xo19+MRppc2FX3xZeTbtIhl5eTbcOTCNecyIc4iJYMjOxi4ut1QfU+3aRQEeO5p0VpaFKyrF6VGh4vVWqrU2gzLcOCOTuymH2MZrbZFlSgyC0Lsq33t81IfqMZUQr49vs/punI7WO/GhpQkPDA4etR3LyArTJ554Ak888QQAT3+9RYsW4fbbb4/IwgjtsXFPflIuqvEVP4WbY6oYym/7d6g3sA8NaTsp81NjKF4vg1wo399jekVeEraVt3Lt82RD2xXr+R/qA4zfo1us3ML0lX0NAbnAUzfXeoQp4/jivRnqyJCdjV3aZqkH0sC4CxMLBXExlBz+uwv3Xs7mEtCoQdVhm8eU3/AmGICH+ziwtiq0lCG9PaZqnA96p3Z5f2bWNZTnEJDa5qBoGuK64pb2JUz9qa2tVd6IiGmaOUP5DhXtosL1iAUL08Dn/XNMjVCfY9o304TfDWnLyctJNuJucxqWitqf/GV4lqr96o28x7Tt3w+fn4FdFTVcbbSq/OKPe6sDp2ZVS/WmYrC/JvC1Xu3pYKyBhKk6yM5GjrcPNuKTY/JtkJxudli8lnG+BPUxVbGW0mYX/rjVijNNLpQ1h+/yLNOg0FFt8dPjF2Xght6pyLIWhfyeQVX5Ie+JjZp0S709pt7jihW257mn4b30iqcrxgOxM2iV0B2bmhxTznZR4tC7WhxBVfnSfUzV2In5V2SjX2YCRuYl+QqfvLw+Oge/MachPdEIu1NAohG4uGvkWqLwIPetNvn9jjf3ScW2gm64bLlyE+zjDS40OtzISDSGdUMh1d2BWfzU0fueEDHJTzUOPLG9TnE7l8AWDlzCVMUpNnt3PVadsvO/QAEtxK3PY8qx7dgeyXhmeDYAwMIuGwkJrSvP1VTli3ueao338GCZYr5QPh/hRjWjgSphumHDBrz22mv48ccfUVdXx5zxWlNTo9niCG1p4jxCnYLAEcoP/H+oKHtM2/6tJpQ/MCsB1/RIYT5nMBgwMi+Z+VysUNki/WH9Q/IAYFYxreqWNVVYf3O3sG4opDzv4psMgDymoUB2Vn9e3MM3rc4leG7UxdSwhKn4bxWHvpaiFNBKmJ7NMeWwu1ppuBcuy2auQSvUeExZRW9a4k31YB0nPIcO7/Gld/9qPeD+mb766itMnjwZJSUlKCgogNvtxuTJk/HLX/4SKSkpuOCCC/Dkk0/quVYiTHg9pk4Oj6lXuGofypduF6WmojKWepKGwvBs6QvLVd2DvbsXdOYTpz9UOXC60cndOoyFVHcHyXZRBDcd0c7W2F3YUGxHhS1yLRx2VvDlZQPAN2eCRSPLYxrUx1T1qrSjnpVXoxI1HlMthOk13ZNxx8C0gMe0tuJqckzVXG9CwWsuWZfQZqeAdcXyNyu8Hnmpy74gCNhW3oIfqzznQlGDE9+W2NGswsVa1ODEhmI7mjSepMLtMZ0/fz4uvPBCrF27FnV1dXjnnXdw991345prrsHJkycxbtw4DBgwQNPFEdrhcgvcYsQlBAvGRGOgWPUe7OGGapWr8tv+nZNsxJkmvg/BqqSNJ8Z2dWHxGSOq7IFfUOdkI25ltHb644UZuG8jX35iXatyDrEcUkV0rBZj1GBfHR3NzpY0uTBmZQXKbW7kphjxzU256Jupb4bZ16ftqLTzG66fRcUkAFDLaPET3GA/vo99rwnlcVqGO3a4/J4eSDIG70frUL4aj6neHZa8R4dUDugvv67mer0SLI8/AEzbasX7Rzw51pP6puCbMy1odgo4LzsBG29RbjG1o7wFBV9X+17zr/M5F8QB98904MAB3H777UhISIDJ5PFxu1wekdC3b1/cd999WLBggXYrIzSlWaXnSuwVE7dZ8gobViW2Gt451IRlx5ux5EgTPj7ajOOiMLX/XWvXFH6rItcWKh7ISAC+m9QNfx+dg/U35WLjLbn4++gcbLolF11SgmNMt/VPw58v4Rs+UdLkQgPDo7LmtI1ZbSxGyvPOnPxEfUxV0dHs7Mt761F+tlCn0u7Gy3sbdH/PP24LPwmyhnGXL85Fjfd7MjVV+eFa22STgSluQyl+ypHpVa0mbzSaHlO+1/MXM4upbXH7RCkAfH7S7kvROlznxKfH5YsCAY+w9X/Nmgrtch+4b02Tk5ORmurx1KSnp8NgMKCystL3/DnnnIMTJ05otjBCW3jD+L7tRYIzzWRAvZ/h1aoq/0yTCw/IePqSQhSm8R7KB4DuaSbcc25bR4GhXeQLtMb3SsELe5Qv7L9ay74Tv3NtDQZkmbBlUp7s3bJk8RPjcQrlq6Oj2Vn/iyMALLU0Y9GVnXR9T96oixzljKp3rUaSxgoGFXJTZtJzWPBMLXp/TGfc922NrzvIP66WPn7UXBYi5TENNegYTvFTuULazLpie8C1h4W4LdW2Wu2EKffh1K9fPxw+fBgAkJiYiPPOOw8rV670Pb969Wrk5+drtjBCW3hbRXkRC1lJj6nOiVT+Bk+Vx7QdCFO1ZGkweu5YvUvxblkqJYTZLirer84RpqPZ2Xg9PFgXdn9TKAgC3jzYFLkF6YCayU96VbDnJMvv99lLszCpbyrW3JCLRy7IwAdjO+P6XtL9mNWsU++BK95jP9SMD0EANpe14KavKnHvhmrJgjfW2HClT8bq3auEmnGvSnDvaty4cVixYgUcDk8Pw8LCQqxatQrDhg3DsGHD8PXXX+O+++7TbmWEpmgvTD3/13uyT2Aon/+OrD14TNWSKRPCUsOG4hZV23urV9nFT1qsqOPQ0e2sXp43rWGlqPgf/rurHPjspC1yC9IBr+nlsSp6jWPPUTggrunu6a5yWbckzL4sGzcrDAlR4wXV3WMqU5XPQ5NTwP9tqMHmslZ8ftKOP+1gp6iwLv3KwlT9erRsYsAdyn/yySdRWFiIhATPS+655x6kpaVhxYoVMJlMmD59Ou666y4Nl0ZoiVph2iTaPk10llafLR7QO1TrP30jU8U8uXjPMQ2FTMbVYWKvFOyqbA0qopJD6s630uZiNuJ3uj137j/XBheJSIX9CTYd3c7q3dRcT/w9X0/vUO6RGuvEhsdUXphqXRzlj+45pt7/h+gyXVlkC7Drn59kV/GzWj8qfW+hfHQtPaZcwtTtdqOsrAwZGRkBCcqTJ0/G5MmTtVsNoRtNKtuHiIWsODRe3OzCwVqH7qF8f4On5lyJF8+LlrC8xIlG4JY+qXjnMH9YkWWUNpa04O511Whk3OC4BOCmr6qY+6IcU37IzgZf3IqbXDha58Tl3ZIkbzZPNjhR3OTC5d2SdG+KLod/8ROrz2m8YVLhMU3SKUKl5IxQ+67FKvq76u7bOHu4hGoid1fytTxj+aSU3jOUUH6iQTtbz3X5drlcuOSSS7B06VLN3piILDapnhESiIWp2GMKAB9YmnQP5ftfaPLS+IMF4bYvaU+o9R6zPAXTttYyRakSYs87IQ3Z2UCBs6eqFSNXlGPS/6pwzRcVzP65a8/YMWJFOW78qgq3rKliDiOIFPGUL8vTwcNbjc9Tla9XKF/pvdU24D9RHxzVkcKks3PDLfq/WvaJRkNLwbr0K7UqDeU+Q0shz/XVJyYmIj8/ny72cUyzSo+pWFCkMvz0bxxoYk770RJ/z+eEnimyrUC8jO8Z21OdIonBwL6pkIO1+YmG0H7oEw38F4KODtnZwFD+K/safG3NjtQ58f6RYK//g5tqffme28pbsalUXX60lsSRLkU2R0jJuwVfjml0jlm173oh5yASQN340lAIt10Ubys+J+NmTWmyYyg/p5ZOc+57gnvuuQcfffQR7HZtR6cR/LS6BFVTGfxR28dU/D5SXrcGnQfx+hu8lAQDPr++K27szR41CgC/HZyOxVfp224mnhAEIF2lMA0ljCPFwVq+u3rCQ0e3s/6et5VFgd/Bx0eDu0WIQ+bbyvknOmlNPPXT58mfVJNjGq3UKbWmasqgdG4xq2XOJAvBJ0z1PXBY6XZ6hPIjnmMKeNqYCIKAyy67DHfddRf69u3r67fnT0FBgXarI3ysPWPHfRtr0OwQ8Nxl2Xj4/AxVr1frMRVvL+V1q2dMQNEScYhoaJckLL2uC9aesWPyN4H9OH/VPxUvj8zRdT3xiNpQvpYhrKP1Tjjdgu6FBO2Fjm5n5YqfePI2o5pjGkfClMe75dUmPBolWue32rcd2iUJ/x3fBV+dtuOa7sn4f+trJLfV22O64qQN11madL+hYXlMlSb/+R8f1XYXntlVj7JmFx4fmonR+eyIpJahfG5h+uCDD/r+/fLLLzO3MRgM7dZgRpund9b5RODTO+twz7lpXM2HvUiNkZRCnE8Yrb6gUkn1rMcTO2CLKCW6pphUh/K1/BpbXEBRgwsDsvUdM9le6Oh2Vu4cruHoLBHNosc40qVcN5++HFOO/UWrm0Io7zr2nBSMPUc66uZF7xxTAPj9ZiuGdNLXNjJzTBUOVv+f87nd9fjobLTi+8pWWO7qziy0TdCw+In7G/Fv8kxEniN1gbl6h6xOXJorPwnIH7Vte8TFT11UNLdnkZloYI7BlMMAYHAOOyeI1UWkI1bii5k1PAvP7q73/f3IBRn4oUpdeFPr7IyDVgcJU046up2Vu9fmKb6Lpmde75CslvB4A00qPKbi3+2SronYU6VNGs/d5jQstbCHfuj5a0eq4yCrzZ6WsLyjrKb7/viLcv/pbPUOAWtO2zGpb3AUJyoe0yuvvFK7dyXCRskVH7y9uv2LhWmCEfjr8Cz81U/0qCEr0YgGhUqpGZdkYkNxC8psLnRKNuLhIRnokc6uxGe3RiKP6W8Hp6O4yYX9NQ7ce24a+mUl4GeVeZ4topsYpUR5JQ5bnbipT1i76DC0Nzvb7HRjb70R2TYXuqUGnsu1jNB8ktGAvdWtSDYZ0DnZGBS+r21x44jVgcGdEpHFuBPlCSIZDfqE3eMplM9jKtsa7PNU5Qdu8/LIHNy1thqVKvonS/GnizOxs6IVlrpgAadnhUN7ST9i3c8p3ePJ5Zi2ugRm94uoCFMvNpsNe/bsQWVlJUaPHo2uXbtqtxqCG5bQ/LbEjse2eKY/vDoqB2P8whVqxYU4xzTRaMBFKioaxSRxdHoa2yMFf7o4i2t/LGEaz825tSIj0Yh5VwTm2apt6SUeI6p2OIOYQ1YqgFJLe7CzTQ43xn1ZiYPWFOQcKseXE3NxwVkbUtTgxPWrK4Nes6OiFdd8Efy4lytWlKPM5kbvDBPW3ZQb9DxPP81ko0F1ahMPcaRLuXpn+qryQ/CYXpqbhH2358NS58DVMr8nD70zErBlUjc0Otzo/3FZwHMquyCqIt5mtEgd+izzrxQVk7u/MxpCmyalBlXBz3/84x8477zzcNNNN2HKlCk4cOAAAKC6uhq9e/fGkiVLNFwaIYfYFS8IAv641YqiRheKGl14fJs14K5Grce0SXTkJhrDq7rj8Waq6YXHFKZazkRrR6j15IjHiIrH06rlkJVaRqlBaztbVlaG3/3udxgwYADy8vIwYsQIbN682fe8IAh48cUXMWjQIOTn5+PGG2/EwYMHw/4cnx6z4eDZ397aKuApv5GJL+6pR2mzelVRZvO85lSjC6//1Bj0PI+Y0GsqXDx5TF0caQeqqvIZ9jg1weP11oIkkwGdGSOpeT5HqJjizNHhvX7y1AgoOark8muNhuCoGqCt95r7qFm6dClmzJiBcePG4bXXXgsQPV26dMGYMWOwYsUKDZdGeGHlg4i9Wg43cNyv1+TxBldAL1K1XjOxlyzRaAi5ifJV+UlcolbN9BBWPimF8tlcx5Ho74/Y6ITrMeUpWiE8aG1nrVYrJkyYAEEQ8Omnn2LHjh146aWXkJvb5m1cuHAhFi1ahLlz52L9+vXIzc1FQUEBGhoawvos4lnxm8vacp0/ORb+HPl3GX1NeWxI1zDz5aWIJ2HKs1Zv8ROPVZXS+jwN8P8ynC9KxiLc79wo4+eON4+pN+WC51qrXPwk/eGNBgNzop+Wxz/3Gbpo0SJMmDAB77zzDiZOnBj0/MUXX4zDhw9rtzLCB8vbKfZisYqb/Ofoqg3lt4reM8EQWvuMCzsn4vnLs7nC7GqKlyiUz09OshEzOCa9eBEL03CnN4m974Q0WtvZv//978jPz8c///lPDB8+HH379sU111yD8847D4DHW7p48WJMmzYNkyZNwpAhQ7B48WI0NjZi2bJlYX0WhoNLU1jOMp6bU730YxzpUq4Cx3A9poByGsDfLs/GYxeoa33oT7gZGXKfLd5yTL3eY57rtKLHVGYXRgPA6tym5fHPLQWOHTuGCRMmSD7fpUsXVFdXSz7fkdlX3Ypb1nhG5v3EOUbMH5a3UywWlISpWNxmKcwgFpNoNKhun/H30Tn4blI3DO2SxOVtVePxZBlCqsqX5qHB/MZ/fUkLFh1oxJrTNlzwaRmu/LwirPfWI5+vvaK1nV21ahWGDx+OKVOmYODAgbjyyivx5ptv+jyxRUVFKC8vx9ixY32vSU1NxahRo7Bjx47QPwg8AzH0JNRxyHpWz0dzJKoa1ITyw5n8pGSSHz4/I6yQebihfDkBpneDfa3xng5aeEzlvheTVChfw0Ofu/gpMzMTdXV1ks8fO3YsLhP0I8HDm60+Qfr7zbX49pZuql7PqsAXFyexhWlbaF9sxFMTDKhX0b4p0WhQ7TH135rn7lNNKD+ZsT/qYyqN2l6mf94pfa6rpcXlSUeJt5ytaKC1nT158iT+9a9/4eGHH8a0adOwf/9+/OlPfwLg6ZlaXl4OAAGhfe/fpaWlkvu1WCyK793alATxJabtdWncn0EK1kjG0tJSWBS6f7S0pkBleQU3hy1HYTIArQ793kML8u1lAIJb/vhTdPIkWpIFFNcbAcinA1WWBX7v3t/Z4xCR/q15jiN/MkypaHS12ZGE6lOwhGGqjDLfQVlJMZQ+dyzhFgRYLBYY3KkQ306Iv+fichMA6dHd1ppqWCzlZ/8K/P3KSkuQWCtAfPy4Ge8jhdlsln2eW5heffXVWLp0KQoLC4OeKy4uxvvvv49Jkybx7q7D0OoSArykP1Y74BYErtwb3z5YoXyRELUzboECQ/mBz6ktAEgwqitOAgLDJFqH8lmFTqHmwHYEov3dNDkFZCWRMFVCazvrdrtxySWXYNasWQCAoUOH4vjx43j77bcDmvkbRPZIEISgx/xRurAAQLeKWqAysP+k73Wbi3k/giry8vNh7scWQo0ONyx1TjQL1dCr0dDAgQORYDQgaV85YNO/6G9IpwR0SjZiSxl/r+JpF2ZgxKB84Mcy2e0G9OuHHukmWCtagX3ylfV9e/aAuadHxFksloDjY66rEU/vrGOG3XmOI3/eTrHh/m9r0eQU8PQlmRg2+BxVrxdj3HZG8rk+vXoCP1WFtf9I4oYBZrMZybtLgy744u+5q9AEWKyQIrdrV5jNZ9O/ROdqzx7nIC/NCPwQeEwIgkH17ykFtzCdOXMmrrvuOlx77bW49dZbYTAY8M0332DDhg147733kJiYiCeffFKTRbUnWJ7MFheQqqJRF8ttzhPKrw4I5YtGjKr0LiYaDapzbvy31jqUr0bYE8HCI9LYnAKy+OdBdFi0trN5eXm+fFIv5557Ls6cOeN7HgAqKirQs2dP3zZVVVVBXlS1sPLA9UYqnFhpc2H8qkqcaJD3pur1/nqRlmDAFxO6osruxs1rqoIGsfgzKCcBX07siq4pJlTalL8HNTmmcvb9oSEZ+NWANDywsQbriluUdybD9b1ScfCOZDjdArNKXy2yofw4vMS4BYEzxzT09zAa2M4yLc8sbj9K//79sWbNGuTn52Pu3LkQBAGLFi3CwoULMXToUKxZswbnnBPe3Ut7hC1Mpa3XiXonfrepBo9tqUVJk+enZoXy1RY/iQ8ktR7TRKP6UZX+4pErlB9mqDdO0rs6JOFW9ncUtLazI0eOxNGjRwMeO3r0KHr16gUA6NOnD/Ly8rBhwwbf83a7Hdu2bcOIESPC+izRGGMsdZS9/lOj7qIUiLwwdbo9bY3y0kzIUKgbyEg0oKsKMWcQ/V8OJadCp2QjsjUqAshKMmoiSgF5ARRvxU+A5/jjyjFVOFC9z7JypqXaRWl5/VXVYP+8887DihUrYLVacfz4cbjdbvTt25dyS2VgCUappH1BEPDrddW+3n9nmlyY0y+4ryQQfKG3M7bxvysWH4hqcw4TQvCYBobylbcPN9xM0id2IWHKj5Z29uGHH8b48eMxb9483Hbbbdi3bx/efPNNPPPMMwA8nvTCwkLMnz8fZrMZAwcOxLx585Ceno7JkyeH9TmSo+Bykrre/vNgcM9TPYj0Ue5/Wil5qP2FA886vdvweUyVN4pFmSdblR+LC1aAV5gqlZd4zyOW2RYAdrso5bflJiQpkJOTg2HDhuHSSy8NS5RGq/FzJGHdWUh5TCvtbp8oBYB1xS1ocbM9pk2iMntWjmldq3SD/VA8puHkmPIYrnCLY8hjKs8FYUzuChcSpurRws4OGzYMS5cuxYoVK3DFFVdg9uzZePrpp/HAAw/4tnnsscfw8MMPY/r06RgzZgzKysqwfPlyZGbytxhjkRJDofzIvX9kF+D2+8BqIk48y1TzSXiuDbGYfWUySH9KnutRRoypV5fAbhd197pq/HlnHRodbgiCgCWHg3sA++M9jpmF106BWRwbNY+p1WrF66+/jq+//hqnT58GAPTq1Qvjx4/H1KlT0alTJ1X7mjBhAkaOHIlPP/0UXbp0QVFREbPx86JFi2A2m/HSSy+hoKAAu3btCttoRgrW1ByWBxQIDL17OWUzoCsrlC8uflLwzIoPMPXC1KD6DtLfVkWi9QZJH3n+MiwL//dtDZqdAqacl4b/HLOhMUKCsZl6mXKjpZ0FgAkTJsi2oDIYDJgxYwZmzJgR1rrFsMSK2sJPtURaGAa9f4Tfz//0VYpuCxL/lsJ7ySCPqTQ5yUY0OvVPEeHFc34FP77qlB2AJ3I5sXcKDsvkIgNtxwfLbP99fwN+Zkzzi4rH9OjRoxg1ahTmz58Pp9OJK6+8EqNHj4bT6cT8+fMxatQoVa0fotn4OZKwWpqwRCQArCoKnoZyotnITDTmaRfl75kNKn5SqRQTDPJ3kKsvC167/wUoElOZSJjKM75XCvbdnoc9v8zDglGdYIxgpT55TPnQ2s7GGuGOt1VCau+ROvoirYv9U7QUQ/l+/+YZFdrl7DY8hZM846BjUZjKLZvnEpkdY51G3JA/1hfsb8Ss7+uV9+MN5TOcYrsq2b3YozL5afr06WhsbMTnn3+OrVu34oMPPsCHH36IrVu34rPPPkNTU5OvPx4P0Wz8HElYzcVZOaaWOgde2BM8AvBYsxEOxj6aeTymfqI4KJSvMsyWIDOStH+mCbnJwe/vb88iMZWJQvnKdE0xoV+WJ1CiZ6S1U3LgzkmY8qG1nY0qjJ9c72ELNayRNIicbXALwMFaB0qaI+NF8z+t1ETBkkwGPH+Z9CjQ5y7N8g1I4BEJXI6H2NJwAJQ8psoLzuEQ+JFkf7UDVolz4P+zd+bhTZTbH/9OkmbtkkI3ltICTS1lK1C2ooCoCAIiiwoXEQXUC4rgUgWvetWrIghXkQu4Iz9FvV5E2URFQa9cVhUFLEsBC8jS0iWl+5Lk90dNaJJJcML9bwAAIABJREFUMkkmybzp+TwPz0Mzk5lvZjlz5pz3PcfKuSrP16a7MaYuvyN8VY8ITuXv3bsXDzzwAAYPHuy0bMiQIbjvvvuwYsUKwTsOZeFnf9b3llOlzsWJT54+i8gy+9O4/PcIAM5jAC/Wcij4w7nQb1lljZ32sxcUAOzr8VyurrWtU1VjX/C5vtLIuz9XFP5xGjKVBXzFkttH8JcA0ZSfQ35+05VddZn/9zXH23Oh4DRotFwxHvG1hcjPd31t+LKPUBJorXWNzoWYxSJWbkJZs+ut4NxF5JtCl/ISq/BzoBHbzoYSvgdVoF9Qntp/GTe0VyNDb29rgvVaNHtnGb74M20aDJrHODxNaHV0zh/oFoUn9/NHzx7sfmWonJCRF+GYyhfS6TAy1AWiHbhpq+e6q0LKuFkvFb526C6/E4oxpjExMdDr9S6X6/V6t8sdCWXhZyuOhYADwZGCGiCv1O6z+DbtYWhr33Xh3O/FAJwdvGozEJfU1mkbMqUaBkMH29/RtRXAKXsjw0UoYTA0lYXhDhUCuDIupHNSa+Cc55A+0JTGv6Zr56bUxq7zTsv7tNMDKMKKq/V4YKcRFgA3dVBjeM8rZW3iysqBC+5nxnp7Lt6JqMH070phsgBXJykxoXdbt9dGMM63WARDa02AipwDQHKMBqeqr1zPka0TYDD43hPbH1g672Lb2VDC96AKdCofAP6+vxz/vsF+sliwIqbBdEoB+1Srt/MGxETQ5KfAy/Aat6l8AR65SFWrgoqQCjnWzLWnslLN2W2U41BpA7qLMMlWsLs/depUfPDBB6iocE43l5eX44MPPsDUqVMF79ibws/NEaPwczDhm4HPl8rvHM3/jlBt4nCRJy3kmN7nS5FZ951X1oB8h8HOQsYYWUmLUUAp5yDjOF7jkhHbdCFOMeiwc2wCNo6Iw/vXtrJbJxB97MemarDrlgR8fmNrfHZjXMiLyLMGn8l5Y3AsZmTo/N52otb+hFMqXxhi29lQwhdsCYZj+tUf/hVxZ4nmh1PrIcTn65EX4tQLGaolRfvsNpUv4JmlknEYk8JO21JAWMT0yhhT4ds9UyPD8z8LC3Z5QnDE1GAwgOM4ZGdnY/LkyejUqROApt7NH3/8MeLj42EwGPDZZ5/ZfW/cuHG82/Om8HPv3r0BXCn8/Nxzzwn/hSHGncPYnDg1/11Q0Qjk7nEuzeDo3PI7wMDh0gZcu6nIaVkrF/vj4yr9lcskQuZcrP+qGAVQ1vT/ri7elgJVrPgqfQSu0oeuDFI4EaPkcHtnLXq2jsA7R92XE/FEksY+lECOqTDEtrOhhM+hCdV1EK5XXzAipkKOnRAnTnpuqf+z8lVyDosH6LHptPshZFLCq1S+l6kGseYtCHZMm6fXly1b5rS8qKgI9957r12nAI7jXBrMUBZ+Dia8DiPPZ67s9ZFK/lyB49gPVw5w7h4j7zgRbyKmzcdr8d3I6XoFzpW534bEhuIQPFjHqEWLcLJaqyli6gti29lQwle6KdCTn1oazQ9naFP5QiKmQRDiJe4y8ULqmKrknOQmQHlCyLnyJWIKhMAx3bRpkzh7/BNr4efnnnsOL7/8Mtq3b89b+Lmmpga5ubkwGo3o06ePKIWfgwlf4Xu+SXPejOUA+CKmzuvUmS3YXVjP+32hjqlaDozvqLH9zefkCik9xZfqiVPLbLVblwyIEaSHCBzWSg1RIpRA0Tk8JIORwg0HxLazoYTvjFd6ajkTIML16nukx5VnobeTn4Qi5GtC3mXHpWqwNr/a9vfVSUo3awcHmYsC+zmJSkER05tTNZ5XkhhCav26KxflDiHjcgVtR+iKV199tSg7bE6oCj8HE74yTr9XNGLbH7XoG6+0vW15M/sNcI668peLcn1ROUa0XLF9TIJdqtzXgAef77r7lgRsPVuLdjo5rmvH1jidcMQacXF0KgHgkR6RWHpQeFtHx+hNFRXYF0Qg7Gyo4HumeSplIyaVDWb8UWVCxyhF2JSS6xJpQqNchfzyRmTGKjDtqivjwYMZMV0yIAaP7W0aYvZivxhBTROGtVPhhnYqbDtXh3i1DC/0C30wgi/CF6eW4cV+MbzDz57NjsayQ5UorTPjpg5qXCMB59pbhDzDbQX2vbxvhFQyEIJXnZ8I7+FzGBf/0jSxoZ1Wjv/dkgC9Ssbb+ssdjo4sX2S21k11nigBr7jZ8RHIjHU/flMlcFYi34MhTi3Dnen+T7QhxEH359sD30PGEOPdOF6dglL5LR0+F7Scr1tIACioaMSoL4pxrtqEHq0iwiZi2ioCWDc6AReqTWink9uNF/RUm9r3yU/O35zZJRKjUzSwAGijFfYQkHEc/n1Da5yuMKGVWoaYQMyI9RK+rPZPExIRo5TBxPNM7hOvxIGJiSitNSM1Si7JCV2eEOJquGtJ6g6xppJ45ZgeOnQIH3zwAQoKCmA0Gp0uWI7j8NVXX4mjLExw5xyeqzbh7aNVeLRnFEzeRkzNniOm7hCSehmYqPK4jtDC+Xxjy1i8qcMZdxGXBI13DxHHtCKl8oUTLnaW72XUGCTH9MWfL+Pcn9VMDpbyd6phFY2CQyeeKi6eUvlikyTQIW2OjONsDT6kAJ9VszrMfI82i6VpuRScal8xeZXK927bYqXyBR/d9957D0OGDME777yDo0ePorq6GjU1NXb/qqurPW+oheHJYfzkZNMx8yVi2nysiDeOKQf+FEafuCtRMTkH3H2V52im0FajfJPACGnR3DG9p8uVc5+pV+Datiq76yMl0vVD6R/Z0TypfDr/QggnO8vrmNYF5zr45JRzi+RwwN3R8yWV/1J/z+n0cL5z3QWZ+QInYrbdDBVepfK9/MFBn/y0ePFiZGVl4cMPP0RSUpI4e2cUk9mCk5cbkaCRQ6+S4XyVCXUmC++boCeH8Xh5I2oaLfBlTkCD+Uoq3RvHNELGf9PlJKnwSM8o7Cmsx+gUNe9buSNCU/k0G1f6NI+4vNA3Bh2jFCirM+O+TB04jsO64XF4I68SuggOVyepcO2mS07beG2QHncYtPil2D5KdepyUx1dY50ZF2tMMEQrBM16bWmEk5218Lg0wUrlt0Q8Tn7i+Wxmhg7z9zqXI7T7Xhibbm9NEN81zRpCslfWc+71GNNgO6aXL1/Go48+yryx9BeT2YJbvirGDxfr0Volw6Q0Ld7Iq0SjBZjbLRLP9rV/A+Ub++nItZuK0MFNBMoV9WaLbYyRkP1YcRXlrG204KYOGtzUQfhMQ6ER09pGz+sQoaV5xEUp5zC7q32npliVDPN7NfXXLuRp+jCkjco2ZtgxelNYY8aT+8rx8clqFNeaMTBRiU0j4gJW35ZVwsnO8k5+IsfUL/yJmPKNFVXIOPwlTYsPT7ARhRcbbxPy4RBfEVIZw3qXepvJFcueCz4vAwYMwMmTJ0XZKctsPVuLHy42lWAqqTNjxW+VtreKf/1WiRKHQaVCIplHjY342oduJc27P3kbMeXDm6L7VoQU6wWAeC/HKBLBJ1kn/OWIr6pD82FXfNGbf/1WaSsPtruwHtv+CG77RhYIJzvLZ5GCOSu/peHr5KebU+0rovRPsJ9pnholnTGhYnNTgn3E5Mb27udVuPLT7jBoxZIUcCoElACyRUy9vF3FijMI9hYWLVqETZs24cMPP4TJ5GZGT5jzqZuxSyYLcLDEPoUZyLGVzYMP3jmmTVdP83IdMg4+taIUWot9ZobOLsz/fN9or/dFiMtTva+cA6UMuC9TeC97hYzDAIcHWL9mf+siPFuo9/NbZpTGHeFkZ/ke4uX1YRByCiHu0uq+Tn4a3l6NIW2aHLJWKhkWOYw71atkeKh7k22IkAFvDo71aT9S5IY4k82OJWhk+Hu2+zG3riYOPZblurZ6ZAgbH/AhKGL65++U/BjTzp0749FHH8WcOXMwb948JCQkQC63j7BwHIdffvlFHGUSpdHDgJsTlxtxbbsrfwfSMS2rMyNBI4OM43gL7LvC6kze20WH2kYL8soacGe6Dgka74cTCJ2VH6+R47Mb47DmWBUyYyO8coKIwDCnWyQazBbklzdiRoYOsV52MHlnaCvM/+4PGGVaDEhUYV6zYt+eojcEP+FkZymVH1x8rWMq4zisH94aJy43IvHPeROO/D07BtOu0kEl5wSXh2IBhQzYPDIOJ9389ua48tM6RCrw3tBWuOu7Uqdl0UoOlRKa/CnEJbHepd7KFiuVL9gxXbFiBZ566ilERkYiIyMD0dEtM+Ll6aQeM9qnBgJphwd+XoS+8RFYNzzOq3I81ohphIzDIz3966IlNJUPAIPbqDC4jecSVERwUMo525hRX2ink+Pp9HoYDClOy0LZHpFlwsnOUio/uHh8GXTziJDLOLtGKnyEa0pfIeC3W3H3/HfVMS9GKcP5araue2v8zdsxpkGPmC5fvhyDBg3Cxx9/DJ2u5RZF5yu625xjxuCl8gFg/6UGfJBf7dV+hE5YErYt0TZFhBFCOsGE82xfXwknO8vX+rDRIqwlIuE9chmHgYlKl22oCf9x9/h3ZfGiGax5anWjvXVfxHJMBR+xqqoqjB8/nnlj6S+eApNGhzFU7tqCisXf9pV7VY5JKWImRmgqnyAcodqmzoSTnXV1dsNhZrNUeXtIK/wlTYvJac6Tceiw+4+7lypXj8JoAePtpYbZx4ipkICEEARHTK+55hocPHhQlJ2yjKdZatUOK9QFqSKvN8ZezPF/ShpLSPhIsbu2aC2UcLKzrkyftzN9CeG008mx8pqmyUkftdASUIHEl4hpJINpRYtt8pN331OI9FMFb2bp0qXYt28fli5diqKiInH2LmHqTBbM2VmGqz6+gPv+W2obw+lp8pNjT3BvJiUFC2/GhXqCwSwFIRFKaslDcSSc7KwrS+nJhhKEVFA7ZBd7xSn5V4Tr652vtJ7U8bWOqTzYLUl79eqFEydO4IUXXkBGRgYSExPRpk0bu39t27YVRZQU+PJsLd7Pr0ZhjRn/PlmDDQVNZaI89bR3dEwde9pLATEnpqhpkgvhIyW1Zhpv6EA42VlXp5Yipr7jz91Cd5r3vDOkla274V/StG67IbrKWuoUHJ7o5d8k42Djax3ToE9+GjduHG8by3Blzs4yu7//+kMZJqVpXdYxs+IcMZWeOfAnYvpQ90i8cqiy2d9s3XBE8Jhi0GKtm1qljRbgTKUpbGf7+kI42VmXEVMJvqwTBB+jUjQ4MCEJ5fVmZOjd2ylXQSuVgsNjWdEYlKTCqK3FAVApPtY71OtyUcF2TFetWiXOHhnB1dhQTyeqwdwU/rbOfK93SOX/rVcUXjhQ4ZMmOSfOxAG1H47pXzMjcaCkAXllDZiRoUOXWGFlNoiWR27PKLeOKQAcNTaQY9oMse3swoULsWjRIrvPEhIScPz4cQBNY8leeuklrFmzBkajEX369MGSJUvQpUsXv/ftKhpOc94Ilmirk6OtgK54roJWqj99gTQ30VapYbZFTL2c/BTslqQEoF99DgeKGzyuZ42aWiwWp45MD/WIgvHudjg+ybte2IOSlBidova8IoDMWAUy3bzd+eOYJmrl+PzGOByf1AaPZ7FbY5EIPKlRCrw/rJXbdeb+z8jbw5sQD4PBgGPHjtn+7dq1y7Zs2bJlWLFiBRYtWoTt27cjPj4e48aNQ0WFby/PzXE9+YnOt6/4lcqnwx5QXAWNrI9blgrY2BxTqZeLAoAzZ87gwQcfRFZWFpKTk7Fz504AQElJCR555BEmupEEg2rbRCl7QyLjrnRG8LZ9nE7BCS7NpJZz0LopUeGPY0oQ3uDpWiusMWP/Jaq72Byx7axCoUBiYqLtX1xcHICmF+dVq1Zh3rx5GDt2LDIzM7Fq1SpUVlZi3bp1fv8OKhclPv44l3TYA0s4OaaWP68WT3XbHRErlS/YMT127BiGDBmCDRs2oHPnzqiqqrL1cm7dujX279+Pt99+WxxVjGOdwe84vrT5Q9rbkk16pUxwuy+VnHNbRF/NTkaBYBwhL0GHSxs9rtNSCISdLSgoQJcuXdCjRw9Mnz4dBQUFAIDTp0+jsLAQw4YNs62r0WiQk5ODvXv3+v1bKGIqLeioBxZXQ1es6W2G/FLbvet9gf0g1zH9+9//jqioKHzzzTeQy+VIS0uzWz58+HB8/vnnoohiHWvhcMfi+s1LK8llHNRyQGgpx3R9BH4UGFnSyDmcrnT9sKeIKREshFxrjsNdWjJi29ns7GysXLkSBoMBxcXFePnllzF8+HDs2bMHhYWFAID4+Hi778THx+PChQtut5ufn+9x3+XlSvA9Yk4WnAagEfwbCHuEHPsm7Ivs19fXe/Fd/wjWfsRALK3nLskBOLfcLi2+hPz8C7jcCDieE6lSUVGJ/PxSFJdGABA+j6T4UiHy5Z6dGoPB4Ha5YMd0165dePTRR5GQkIDS0lKn5cnJyR6NWUuhptGMOpMFz/x02e5zx9nwGgUn+KF8lV6B/5wUVjBZJedwttL1xUGOKREshJQTe2JfOX4tqcfDPaIE96wOV8S2szfccIPd39nZ2cjKysKHH36Ivn37AoBTFQCLxeKxMoCnBwsARF4sA4qcbVbb5BTgZ3ZrtD7SIxJLD1Z6XjEAWCDs2AMAdp6z+1OpVMJgSBZflAP5+fnCNYYYMbX+LKsGjpU5fZ6UEA+DIRLGOjOwhw0fqVGpRevk9ogqqQDOVwn+XtukRBgM/netE5zKb2xsdNsmr6ysDHK5iL0uQ4w/rlt1owX/PFiB/ztub5QduyTpvGiT0EWvwLkqYeFVjYJDvZv6Y+SYEsHCsUC1K/59sgbjvypp8XVNA21nIyMjkZGRgVOnTiExMREAnAr5FxcXO0VRfcHVpLYNv9f4ve1Q8mjPaOwbl4B94xIQz1Dx9BZ+awUcV+MxZQyOMd15sR5pH1/EG0eEO6VACArsZ2Zm4ocffuBdZrFYsGnTJmRlZYkiSgr4cw9XNViw6BfnWa0qhysz0u0EJfu/U6MUeDxLWM1QjYJDbk/X65JjSgQLb661c9UmHC9v2eNNA21na2trkZ+fj8TERKSkpCAxMRE7duywW757927079/f531YcfVuvOSg/zP+Q4mMaxpala6PcNuiMhDoI3zfYbyGHSeaRVw1rrE6a6yVJ/bl2g7KrPyPPvoIp0+fBgDMmjULGzZswOLFi20pJrPZjOPHj2P69Ok4cOAA5syZI44qxqlxkZ5XOjibrVSuD7+1FBMHC17oFwOFjMP4TlqkRnmOlsSpZJhi0KKdln9d6m9PBAtvX4I8dVYLRwJpZ5988kns3LkTBQUF+PHHHzFt2jRUV1dj8uTJ4DgOs2bNwquvvoqNGzciLy8Ps2fPhk6nw8SJE/3/YWEaoWseX/DUcEVs/priuVyhlTcHx9r9/WK/GLHlEM0YmaxBFE+wyTYrP8h6QoEXSWD323G38P7778cbb7yBlJQUTJgwAWfPnsULL7yAl156CQAwYcIEAIBcLsfzzz/vNJ6JVXacq/Wrx71j9ycrjmNM49ykgeZ1j8SEThr8/nsBhnSNBAC008nxv7EJKKgwwVhvdtlFIk4tQ2qUAnvGJ+BMhQmDNtin6lhKKRBs423L2nv+W4q/pGlxT5dIvzqUsUQg7ez58+cxc+ZMlJSUIC4uDtnZ2di2bRs6dOgAAJg7dy5qamqQm5trK7C/fv16REX539EtXN8xmlvtYERM70rX4o8qEyanaZFiEjbPAADGd9TgTKUJewrrMDZVg6zWLXv8dqBRKzj854bWGPGF/XPZasZ8iZhGyJqa9rCCLBiz8h3HCM2bNw8TJ07Exo0bcerUKZjNZnTs2BE333wzUlJSRBEUanYX1mHc1yV+bcOlYypzdEz5I5pyrmlCQodIBerU9tvSRcjQtZUMP7uZoR+nadpu1J/r8m2fIIKBtxHTvLJGPLn/Mk5XmPDyQH2AVEmLQNrZd9991+1yjuOwYMECLFiwwGvdngjXqlDNzXgwfuPfekcj/k+b7s0EcoWMw6M9owBQ2+hgMSDReVa+/M8LRubDzJUIGYcGhm4ksXwLrytatm/fHrNnzxZn7xLkwf8Z/d6GK8fUMYUe52LMT4SAcLi7FmnuIrGAeAOUCcIT7urpuuObc7UiK2GLcLCz4TrZpnnFgmBUOiNzzRaOUc6ef0aqfTGFYqXGg4VYej1uxlPZkHAjX4TJF9UNriKm9n/HuRhjqhBwzJO0cgxIUPIu8+SYsnaxEy2PmhbWUD0c7aw5XAeZNiMYv5HMNVu8OTjW1gHpllQNMv4sgedTKp8xuxC0Avv333+/4MH2HMfh/PnzfosKBWV1ZmwsEKeMyabT/NtxjJjGunBM6wWG7v9vWCukf3zR6fPWHiOmgjZPECGjzov01e+XG/Hd+Tr0S1Ciays2x9GFo50N14hpc4KRZQ3Hl5ZwZlxHLbJaK1FaZ0bvuCv2yJcXDCHZUykRtFR+nz59kJqaKs7eJEqD2YIhG4twxk1Rem845iLq6jiZw1Vo310N0ua4Kk/hKWIq1gBlgggU9QJvxTOVjbhmQxEqGy1QcMA3o+ORFcefSZAy4WhnGRoa5zNBSeUHfheEyHSMVqCjw2c+RUwZiyIFLWJ6991349ZbbxVlZ1Lls99rRHNK3eHomHaM4j/8Qos2a3guWp2Cg9ZDrj4lMnwaIRDsoeAAT5n6OoFP/JW/VaLyz401WoDH95bjq1H+F4cPNuFoZ1uAXxqkiGng90EEHl9Oo5tS55JEHqwxpoFi4cKF0Ov1dv/S09Ntyy0WCxYuXIiMjAwkJSVh1KhROHLkSEC0HCoVXhvOHxzrlvaKi+At4TE5TVg/XQVPyDVG6fzZU72jbf/vHRdhG4xNEKHAU0QfaHIyXXVSac5XZ+0nSe0tcl2tggguLSFiGgwY800IF/gyJIO1muMhm5UvJgaDAZs3b7b93bzV3rJly7BixQqsWLECBoMBixcvxrhx47B//35Rauw1J1in3vGBzHEcNo+Mw39O1mD/pXq008mRoVfgllSNz/vQ8QxKebhHJDL0ChTXmjGxk4bGLBEhpbVahos1nserVDVaEM3zotWcyAgZgMBnOwjvIb9UHKjudMuFL/gkZYRM3Ba0HVG24uvOFQpbv+bmWCwWrFq1CvPmzcPYsWMBAKtWrYLBYMC6detw9913i6ojWKeeb1JSZIQMd2focHeG6/7Y3sDX5pTjOIxK8d3ZJQgxaarf67n6xY1bLmHXLQluX6TctfUlQotjfVbCN+gKb7m01MlPbn92WVlZQMc9FRQUoEuXLujRowemT5+OgoICAMDp06dRWFiIYcOG2dbVaDTIycnB3r17RdcRrACi0LGj/hDpZacdggg22fHChpIcMTZi2x91btfhawHIGoG2s6GCUvniQAmulgtr5aLECvCGLGKanZ2NlStXwmAwoLi4GC+//DKGDx+OPXv2oLCwEAAQH28/iSE+Ph4XLlxwu918b1pj/Ll+WVkEgMCPu6y5dB75dd73F3P/m+zHo3L11V4fA7EI1X69gQWNVljR6klnbicFXj7VNFNeJ7fgRk0RlkLYOOq1hwrRsdb1uFFLrRKOZuz48Xynh7nQY2kwGAStR3iG/FJx8KVjEBEesFZzXKyhByFzTB37PWdnZyMrKwsffvgh+vbtC8B5sLDFYvE4PtKbB0t+fj4MBgNaXy4H/qgU/D1f6WVIQcdo7w65VaNLdp6z+zNRHwWDoZUv8vzCo04JwIJGK6xoFaLziTQLOrSpxvHyRkw1aGGIUQB7hdXhvCzXwWBw3YYz5nwpUGxfNzgptTOilVcsOivHMtxgOWLao1UEDgZpUqwnGAuaESLSUic/ScYfj4yMREZGBk6dOmUbd1pUVGS3TnFxsVMUVQyCNvnJRQtSMaExd4TU4DgOU9N1+EffGKTrI7yafHem0v1YVL4OUcW13mclCPFh1S/tFCVHVpx0KpcwNv+FEBHWHudh55jW1tYiPz8fiYmJSElJQWJiInbs2GG3fPfu3ejfv7/o++b8dE0dy0C5IhjjP1Nd1EYlCBb5o8r9jHu+eqfFtTRLXwqwGjF9Z2grqCTkDUpHCeEv3k5mYq3Avlip/JA5pk8++SR27tyJgoIC/Pjjj5g2bRqqq6sxefJkcByHWbNm4dVXX8XGjRuRl5eH2bNnQ6fTYeLEieKL8fNY6pWeXdsb26sCUqbpsawrpbM0cg7T0sWZ3U8QgeSmDmpB69V58DFreBzTqgZGPaIwg9Wz0CtOKakopYSkEH6i9PLCYm1WPvOTn86fP4+ZM2eipKQEcXFxyM7OxrZt29ChQwcAwNy5c1FTU4Pc3FwYjUb06dMH69evF72GqRhoFBy0Cg5VLtrZTEvX4ole0bzL/OXh7lEwmS04cbkR93aJhF5g9JYgQslrg/RI1FzGe8eqPTow9SaLy7FWfBHT6j/vw3qTBRUNlNYPFWYfy0VxCL1TK6X6kRKSQviJt46mWHVBgwXzBfbfffddt8s5jsOCBQuwYMGCgGtp9DPnpHXhmKbHKLBvvHOdVjFRKzg81ScmoPsgCLGJU8vxSk4s+iWoMOuHMrfrVjaY0UrO30aXb4xpdaMFn/1ejft3GjEoUYkXHZtWE0HBV6saIQPqQ/w+4eoB+/KA4NtaaogSPjS1JRd+Z7AWMWV+Vr6UENqX2xXxGjkKeTrZqBgbH0IQwUZIgP9ygwWtXGT+a11ETBceuGyLnBKhwdf3fYWMQ32IB6jylen5bkw8suKUwRdDhA0RXqfy2fIhwm7yUyjxN9v3ZO9oJGmcIzoackwJwi03Jquh8zApsMLNmFG+MainLjfatTz9voQ/2koEFl8bP0mhdqOMJ0rZo7V0ZuoTbOJtBJTKRbVghLydD2+vwqxMHbJaR2BSZw1u66xBz9YReHlADDJjI3CV3jn4rKLnIUG4RRchw7tDW7ntBlXhJq/Ll8r/ufhKQf5t5+qw+iwlhkJYG2PMAAAgAElEQVSBz6l8CaSu+R6woVdFsI67LGpbrbM7xlojR7lI9y5ZbDRNknDH7K46vNhP73YdPsdUw9pVRRAh4MZkNW5MVmPgZ4U4YnSuW+o+Yuq87ECxfWH0jlpK6YcCXyc/SSNi6vwZjfUk/MVdar6NVo7z1fYv4axFTMVCAiYg9HhK5Wvlng9Tht454kNjTAlCOK6GhLqbWc9XLqrSYUOdtDQzPxT4Pvkp9HaTHoxEIHCXym+jdU6xsjb5Sax3N8Z+dmDwlMrXCmi/kMETMVWTY0oQgjG5uA8r6vk/bzRbIGTeYhs1RUxDge+Tn8TV4Qt8Y0z5uLatKsBKiHDCXR3TNjpnx1Ss1HiwEGtejQRMQOjxVJpEKyAl347noiqro0gNQQjF24gpX7SUD7WMHNNQwHTEVKCEf/SlUn2EcNyl5hPUzu6YkiEP7fY2DaINPaAxpgAaPDzghLSn4xt/VFBBrREJQigmF+9xl11ETIWWeaOeE6HB14ipFPqDC3VMu7WKwPN9o/Hk/suBFUSEBe5S83wvZFJ4SXNHSqQcbwyORaMFSLh8RrTtksmG51S+r6mlSuo6QxCCMbmYLHPZVcRUYJ1SlqIOgWbp0qXQ6/XIzc21fWaxWLBw4UJkZGQgKSkJo0aNwpEjR/zel6/louQSeBh7o4BvbCBB8OHO0eRbFCHx4YAyDhiQqMLVSSrRxpcC5JgC8Dz5SWg3g4X97NM6j/aUXvtUgpAqf8/mT4u6KpRfLzAhQan8Jvbv3481a9aga9eudp8vW7YMK1aswKJFi7B9+3bEx8dj3LhxqKio8Gt/vr6WS2HChzcPWXeBe7UcSI3yznHt2Gz9nEQq6B9OuCoheU8XHXQ8qYJAZw/8vdcC9Q4pARMQejxFTIWevGlXaTHwT0OSHR+BCZ20/kojiBbD2FQ1hvJMJnFVzk1odyCKmALl5eW45557sHz5cuj1V0rfWSwWrFq1CvPmzcPYsWORmZmJVatWobKyEuvWrfNrn76Wi5JC+lLo5CeA3zEdk6JGr7gIvDWkldeR47eGtMLARCWGtlXhlRz3ZQoJtuCb/DQuVYPcnlFI5GnSE+h7wd9ovzf3iVfbDchWGaDeZMHqswrM/L7Uqe6hI0IvDq1Chi9GxqHgL23w9ah4xNLgNoIQjFYhw/rhrbF8kP3DuM6FAyrUMaXbEDbHc8iQIXafnz59GoWFhRg2bJjtM41Gg5ycHOzdu9e/nbI8K9+LdfmGoLw/rDV2jEnAmBSN2zq8fGTHK7H1pnh8fmMcruIpQ0iwC58vsfraVkjQyJHE4yQG+l7g61jpDYFym1vs5KfX8yqx8rQSQI3Hdb0Z5sFxHPSq0L/xEwSLyDgOrRw8Sb62o4DwVsLKFp7KX7NmDU6dOoU33njDaVlhYSEAID4+3u7z+Ph4XLhwweU28/PzPe63pl4NX2IfDbU1AEI3bjM/Px8lxQoASqfP+bhwUQ5A5XLd8noNvHmEezq2Qo59qGFBo5Vgak3n7K+VBKXZtv+aWg6Axm790kuFcLy2xETZ6N+91tBQb3f8hB5Lg8HgdnmLc0wtFgsW/1qBhQeEj5/qST2SCSJoODamcJnKF1wuym9JzJKfn4/nnnsOW7duhVLperyiY1URi8XittORpwcLACgPFQJw7uTliWidFjDWuV2nlUqG0gCV4zMYDEhsrAROlTt9zkecqQo4YXS5rmnnOa/374r8/HxBxz6UsKDRSrC13t/Zgg+LinC8vBEKDlg5JB6G9moAQIrJAvx43m79NklJwPGygOlp2yoSKPMcnHOFRqmEwZAMQNxj2eIcU47jsOZYleD1775Ki+TIFneYCCJkONbCq3XhgDbQGFOP7Nu3DyUlJRg4cKDtM5PJhF27duHdd9/Fnj17AABFRUVo3769bZ3i4mKnKKq3+Oo2epps2k4rxzdj4vHTpXooZMCkb0p93JNrvBna56qahDvWD2+NnEQVHtptxEcnqr3+PsEmETIOO8bEY8f5OnSOVqBL7JWgF18N0HJPRda9QME514rW+2kcA1X/v0V6XLEqmVNP2ub8dlsSODQV9qYxPgQRXBzHhNabLThqbMBH+dWoM1swsZMW2fFKj40xgKYEqhTqYoaKUaNGoVevXnaf3X///ejcuTMefvhhpKWlITExETt27EDv3r0BALW1tdi9ezeee+45v/bta7koIZNN22jlGJ2iwaFS9/MDfIXzIvXu7QSVjlFyDGvXFCW7OUVNjmkLQxchw+gUjecVIXy4khAUMqDRYVgU34Qrb6DJT0FEp+DQVicnp5QgQoBjKr+oxowbt1zCssOVeD2vCiO/uITfLzcKSuWr5VzA3upZQK/XIzMz0+6fVqtFbGwsMjMzwXEcZs2ahVdffRUbN25EXl4eZs+eDZ1Oh4kTJ/q1b19n5Ss8nLDmiwP1APPG17ylo8YuKj8t3X01lqhmnve1bdWIavbmNL6jMIeFCE/uvurKtaPggFEd1KJtW8FxuK3TletLp+AwKsW/7QeqaECLjJjOzIjEQ7uNLpcrqV4yQYQMR8f0TKX9a36DGdh6thZttJ7dEnWLtHDeMXfuXNTU1CA3NxdGoxF9+vTB+vXrERXlXx3mYNQxDVgdRS+2G6OU4Y3BsVh6sBLtdXI8nhXtdv1o5ZWNqxUc3hoSixd/rkC8Roa/93H/XSK8eSwrGoU1ZpyuaMS87lGiVvaRy4Cn+kSjrM6M89UmPJ4V7X8qXyRtjrRIsz2hkwYv/3rZZTpfLfFuCwQRzjg6pnwcMzagtdrzbFW6l53ZsmWL3d8cx2HBggVYsGCBqPvxNZXvySm0i5gG6PR6u9lxHbUY11FY3epoB2dgRLIGI5IpUko0DVH58LrWtr/F7B6p4DgkRyrwn+Fxts+Kavxrm04F9kUkWinDrlsSeZdpFVzAxk0QBOEZIS/xR43CUvlCnFwiMPj6SPXUkpRz8X8xEfOBa4ixj/8M42kiQRB8iHl989VE9fc6D1QhvhbpmAKAXiVDisbZdGroQUYQIUWIM3nU2CCowD7dz6HD14ipp1PWfHHgUvnibfil/jFQ/Lm5lEg5pqbrRNs2Ed4IuQ6FjkPlG7vtrwNoCkzFtpaZyreikztbTm1LnsJLEBKAr2yKI+X1FhTWeLaKFDENHQFzTO1S+QGaFSziZq9rp8Z3Nycgv7wB17VT0zVJCMbTlfL1qDhUNFiw5Uytx23xR0z9uxYbBZbs85YWGzEFAL7ypDoFGQ2CCCUqgV5BSa1nx1RD93PIsPiY6JN7mpXv4v9CELq+2FdNt1YRGNdR6zS+lCDc4ckU9ktQQaiJ43NM3d1qQnwhx7qoYtGi7xK+iCk9yAgitAitiiHEMaXoVOhwDKYIjUJGyIAMvetknp1j6uXp9WTfH89qqkQQqCECBOENQq5voVFPvlS+u2+213k2xBQxDQB8x11LjilBhBQZxwkqGVRS63lGKc3KDx2Ojyyh2SiOA5YPikWvuAj0jnNfS9pbB1LFY/MX9otBB40ZY1PV+GtmZNN2vdssQQQEIZe3UBMn9/KibifAMW0IUMSUxpg6oKUHGUGEHCF3oZCIKb1ohg7HYIpazqFCwJNMBg59E5TYMSYBAKBfbd9r3p8C+01dmuw1zOoaieuVF2w9v4HAjV0lCG8Q8uIl9OXMU+MKR9pSxDQ06Hjccpr8RBChR0i70ZI6zysNpdI8IcPxmSV0mJSn56c37UIdEfrA6xRNXVaI0CPkShd6N/CNMXU31CknUelxm40BmpXfoh3TSL6IKd/ZIwhCcgiJmE41CCt6ToiPz6l8T8v9iB14mlhlpUdrJQYlXXkwv9Q/xvedEoSPcAKuV6HR/Qie0KpKzmGKCxt5bTs1+sa7H0rT6GvpDQ+07FS+gs8xpYgpQbCApxmhD3WPFGTYicBgdnhoCY6Y+rncHTIv4g7rh8fhizM1iFPLcU0birwT0kTwGFMX6y0fpMfIZDXu2F5q97lSBmwaEY8tZ2qQpJVj1NZip+9SHdMAEEmTnwgibMmIdf+2TwQWx/cGobbVY0tS3+QA8K7hgkrOCW4zShChQvAYUxcryjgOo1OcW+IqZBzUCg4TOrm+B0TsmGqvKTCb9Z6lS5dCr9cjNzfX9pnFYsHChQuRkZGBpKQkjBo1CkeOHBFtnwlKKhdFEOHKVTEt+r075Dhm+YQ6pt4Eub1NJL48QG/39ysD9S7WJAhp0K0V/wv2jIymDmJC7xdPt1/3ZvvpHC1HtMN8mzvTnR3UhnCe/LR//36sWbMGXbt2tft82bJlWLFiBRYtWoTt27cjPj4e48aNQ0VFhSj77ah1dveFFvcmCELapLuphUkEHsdHlmipfD9M9NVJSsztFomUSDkmddbg1s7OkSKCkBKvDNQjXWdGZqwCt3XSoGOUHMPaqvBwD2vNXYF1TD14e6/k6NGjVQQyYxVYNijWaRhUbs8op+8EqsB+yC13eXk57rnnHixfvhyLFy+2fW6xWLBq1SrMmzcPY8eOBQCsWrUKBoMB69atw9133+33vmN4XkQu1niujUgQhPShiYyhha9clBA8jQtuvtTb56JcxuHZvjF4ti9NZiLYoG+CEmt71dqVM2uO0Pc0TxP/suOV+O/YBJfLkyMVcC62FhhCbrmtjueQIUPsPj99+jQKCwsxbNgw22cajQY5OTnYu3dvwPRQxJQgCMJ/HB9g/ROETSBytMDdHVKZzUuARQvpxEAQYUy8Wtg9IIZrE6zbLaQR0zVr1uDUqVN44403nJYVFhYCAOLj4+0+j4+Px4ULF1xuMz8/3ysNs1PqsfL0lbIgOcpLyM8v8mobgcbb3xQqWNDJgkYrrGgNhM5p7SOw5g/fJy891LHeTpdQjQaDwed9EvY4zsqf2EmD1w5XoKDCfVbKMbDzz4F6jNp6CfVmQK/k8FCPKylFvUqGW1I1+LyghndbXWMV+K2sEQDw2iAaT0qEH4laOW7qoMYXZ2rdridGpFMh41AfoHGldvsJ+B5ckJ+fj+eeew5bt26FUum6kKtjWsdisbhN9XjzYMnPz8eCa1JQrizHryUNmNRZg5Fd20qqxEx+fj4TD0sWdLKg0QorWgOl85kOZlTuNuLT3/kdDj6Gt1fhXJUJfeOVyO3fxpbKZ+VYhhuOzy+ljMN3YxKw5UwNOkcrMOIL5/IzgHPE1NoF6peSegxpo0KCxr6cyttDYl06pl+Nisem07Vor6OST0T4subaVthYUIMZ35e5XEcMfzJYc8ND5pju27cPJSUlGDhwoO0zk8mEXbt24d1338WePXsAAEVFRWjfvr1tneLiYqcoqj9ERciw4upY0bZHEIT/xKpkeGdoK/SJr8QT+8oFfSe3ZzT6JnjuVkIEB8fnoIxrinBOMejcfo/v2de1VQS6upid7KoMDgBERsgwOY1KPhHhTYSsqazThyeq8e25Ot51xIhzyoOUyg/ZAJ1Ro0Zh165d+OGHH2z/evXqhQkTJuCHH35AWloaEhMTsWPHDtt3amtrsXv3bvTv3z9UsgmCCCJCJ8wAwRv/RAjDMULjmIi6qYOa93tiDfN/pEekOBsiCEZwd+tYROjSxNc9KhCELGKq1+uh19uP+dFqtYiNjUVmZiYAYNasWVi6dCkMBgPS0tKwZMkS6HQ6TJw4MRSSCYIIMmovWpa76/tMBB+niKnD30/3iUZhtQk/FTfYfS7WWfxrJjmmBGFFjFR+RJCGOYa8XJQ75s6di5qaGuTm5sJoNKJPnz5Yv349oqKc62kRBBF+eBMxpa5t0sJTxDRDH4FvxyRAv/qcw3r+n8fecRGI13jxVkMQYYDbiKkI2w9WKl9SjumWLVvs/uY4DgsWLMCCBQtCpIggiFCi9sLZ1EWQYyoV+NKGQs+OGNlCJZX9I1og7t7pRMjkB23yE43KIghCsvBFTKOV/NbRmz7oRGDhewYKjYSKcRZpvDFB2CNWuahgQLcvQRCShc8xjVHymy2hLS+JwOMYnfHmeSaOY0rXAtHycHfVi1IuKtxn5RMEQXhCqGOqVXCCe0a3JN566y3k5OQgOTkZycnJuOGGG/DVV1/ZllssFixcuBAZGRlISkrCqFGjcOTIEb/3a3b425sHDUsdaghCUrixgWJETIP1wke3L0EQkoVvjGkMTyqf0vj8tG3bFs8++yy+//577NixA4MHD8aUKVNw+PBhAMCyZcuwYsUKLFq0CNu3b0d8fDzGjRuHiooKv/braeKTO8Q4k8FKORIEKzh2YvOFF/vF2P39j77Rfm+TD3JMCYKQLIIjpjTxiZdRo0bhhhtuQKdOnZCWloannnoKkZGR2L9/PywWC1atWoV58+Zh7NixyMzMxKpVq1BZWYl169b5tV9/UvlieKaUyidaIoGelT8wUYnZXXWIV8swMlmNqR6aZfgKOaYEQUgWoY6pjsaXesRkMuHTTz9FVVUV+vXrh9OnT6OwsBDDhg2zraPRaJCTk4O9e/f6tS+zw2PQm7MjxpAMSuUThD1izMqXcRxe7KdH/uQ2+Oj61tCrAnOj0e1LEIRk4SuwH80THaWJT6757bff0K5dOyQkJOChhx7CBx98gK5du6KwsBAAnFo8x8fHo6ioyK99OkdMhZ8fX86kyuE66RVHrWmJlkefOP62vYA4EdNgIak6pgRBEM3h6+YUw/OWTsX1XWMwGPDDDz+gvLwcGzduxKxZs7B582bbcscyThaLxWNpp/z8fLfLqxoB4EqPeovZ7OY79r3siy9dQn7+Bbfbd+RZgxzzj6oAAK0iLLhGfgEeJDrh6TdJBRZ0sqDRCitahei8XgX8S6FBeaPz/VtdUxPw3yp0+waDwe1yckwJgpAslMr3H6VSiU6dOgEAevXqhZ9//hkrV67Eo48+CgAoKipC+/btbesXFxc7RVEd8fRguVxvBvZccS4Vcpnr7+y07/yUmBAPg8G7dqIGA9A1tQ5HjQ24OUWDRK13XZ/y8/M9/iYpwIJOFjRaYUWrNzp3dTBhy+kaPLa33O5zlUoDg6FDIOQBEPdYUiqfIAjJIueZxKLnm5VPjqlgzGYz6uvrkZKSgsTEROzYscO2rLa2Frt370b//v3924fjrHwvvuvrmbymjQr3dIn02ikliHCinU6OezO9e7GTGhQxJQhC0jzYLRKvHa4EAMzI0CGat44pvWPz8cwzz2D48OFo166dbbb9zp078cknn4DjOMyaNQtLly6FwWBAWloalixZAp1Oh4kTJ/q13xglh0vT2sJiAY6fOIHOndMEf5fK0RKE/zybHY2//3jZ9vffswNT2ikQkGNKEISkeTY7Gte1U8NssWBoWxV2nK9zWodS+fwUFhbi3nvvRVFREaKjo9G1a1esW7cO1113HQBg7ty5qKmpQW5uLoxGI/r06YP169cjKirKr/1yHAfrHDWVzLuINp1JgvCfqQYtfrhQhx8v1ePWTloMTGRnQiA5pgRBSBqO4zCkrcr2N5+TQ6l8flatWuV2OcdxWLBgARYsWBAkRZ6hiClB+E8rtRzrhseFWoZPUP6LIAim4Jv8xFdCimCT1CiKlxBES4YcU4IgmOKqGAUMMVeclwgZMDxZHUJFhD8836ytoSFGgWubRccJgmh50KspQRBMIZdx2DwiDp+crMblBgtGJqvRszU746cIe+7vGokOkQqcrzZhUmetKJ2fCIJgF3JMCYJgjkStHHO6+zdBh5AGHMfh5lRNqGUQBCERKJVPEARBEARBSAJyTAmCIAiCIAhJQI4pQRAEQRAEIQnIMSUIgiAIgiAkATmmBEEQBEEQhCQgx5QgCIIgCIKQBOSYEgRBEARBEJKAMxqNllCLIAiCIAiCIAiKmBIEQRAEQRCSgBxTgiAIgiAIQhKQY0oQBEEQBEFIAnJMCYIgCIIgCElAjilBEARBEAQhCcgxJcICs9kcagkEQRBhDdlZIhiQY0owzcWLFwEAMpkMFgs7lc9Y0koQRMuG7CwRTMLSMZXyW11lZSVKS0tRWloaailu+fXXX/HWW2+FWoZbjh8/ji5duuDpp58GAHAcF2JFrqmtrUVlZSUaGxsBNGmV6nUqVV2OsKIzXJHy8Sc7Kx5kZwODVHU5EgqdYeOYFhQU4K233kJjYyNkMpkkT/qRI0cwbdo03HjjjZg6dSrefffdUEvi5fDhwxg6dCjOnj0baikuOXjwIIYNGwatVotjx46huro61JJckpeXh2nTpmHkyJGYNm0ann/+eQBN0QepwML9A7CjM1xh4fiTnRUPsrPiwsL9A4Rep3z+/PnPBHWPAeDkyZO4/vrr8eOPP8JsNiM7OxtyuRxms1kyb3d5eXm46aabMGTIEIwfPx6XLl3Cjz/+iNGjR0OlUoVano1Dhw7hxhtvxOzZs/Hcc8+FWg4vhw4dwogRI/DYY4/hb3/7G55++ml069YNGRkZoZbmRH5+PkaOHIkhQ4Zg9OjRUCgUWL16Nfbu3Yvrr78earUaFoslpNcpC/cPwI7OcIWF4092VjzIzooLC/cPIA2dzDumRqMR8+bNQ8eOHZGeno5du3bBaDSib9++kjnpFy5cwOTJk3Hrrbfi+eefR7du3RAfH4+9e/diyJAhqKmpQVRUVEg1AsDZs2fRr18/zJ49G88++ywaGxuxfPlyrF69Gjt27EBRURF69uwZUo2//fYbBg8ejDlz5uCxxx6DXq9HXl4eDh48iOHDh0OlUoX8fFsxmUx45ZVXkJqaiiVLlqBbt27o168ffv31V2zZsgU//fQTJk+eDI7jQmY0Wbh/WNIZrrBw/MnOigfZWXFh4f6Rkk7pxLh9pLGxESkpKbjjjjuwaNEi9OjRA5s3b8by5cslEy4/e/YsRo4cibvuusv22bfffouDBw9ixIgRuP322zFz5szQCfyT8+fPQ6/X4/z582hsbMT48eOxadMm1NbW4ujRo1i2bBlyc3NDpq+hoQFvvvkm5s+fj6eeegoAoFarMXjwYPzvf/9DSUmJzfhIAblcjlOnTqGqqgpA00B8tVqNAQMG4K677sLJkycxe/ZsAKEbt8XC/cOSznCFheNPdlYcyM6KDwv3j5R0Mh0xtVgs0Ol06NWrF7p37267GI8cOYKdO3eirKwM/fr1g1wuR319PeRyeUh06vV6pKenIzU1FQDw6quv4pVXXsFLL72EqVOnIiMjA2vXroVKpULv3r1DohEA2rRpg/79++O1117DM888gx49emDVqlWYOnUqxo4dC5PJhA0bNqB79+5ITk4Ouj65XI7+/fvj+uuvBwDb21t2djbWr1+Po0ePYvTo0ZIYU2QymWA2m3Ho0CGcO3cOBoMBbdq0QUFBAe69915MmjQJ2dnZ2LZtG0aPHg2dThcSnVqtVvL3Dyv3ebjCyvEnOysOZGfFh+ysdzDpmDYPJ3McB7VaDZlMhoaGBuh0OgwcOBB5eXm2g9mzZ088++yz+P777zFs2LCgarRYLFAqlYiKirLdyBcuXMCUKVMwZswYdOjQAcnJyfj444/Rrl07DB48OCj6+HTKZDIkJSWhd+/eqK2txfTp09GtWzdYLBaoVCq0b98eixcvRv/+/dGjR4+gazSbzdBqtTCZTJDJZDbdZrMZ586dw65duzBixAjExMSELDVi3S/HcZDL5YiMjMR//vMffPHFF/j888/xj3/8AxMnTsTjjz+O1q1bY+HChRg9ejTatWsXVJ3NU1o6nQ4cx0nq/gHYuM/DGRaOP9lZ8TWSnRUPsrO+wRmNRmnE4wVy4sQJrFmzBmVlZUhOTsbdd9+NhIQE23KTyQS5XI7Lly/j6aefRl5eHmpqanDixAls3boVWVlZIdfYHIvFgqqqKtx1110YN24cpkyZErRxMI4677rrLiQmJqKxsRHnzp1DUlISVCqVLWVz/vx53HnnnXjmmWdwzTXXBFwfn0bHY2k9Vn/88QdycnJw//334/HHHw+KNnda27dvj7vuugtJSUnIy8vD9u3bUVpaivT0dEyaNAkWiwUHDhzAgw8+iI8++ihokZHz58+joaEBKSkpMJvNTlEP62ehvH8ANu7zcIaF4092NnAayc76B9lZ/wh9LN4Ljh49imHDhuHcuXM4ffo0tm7dioEDB+Kbb76x3dTWAbrR0dF48skncfHiRZw9exbbtm0LyskWorH52ByO4/Dqq6/i5MmTNiMUDGPpTqdCoUBKSoptFqv1zXT16tWorKxEWlpawPV50mg9hhzHwWQy2QzUhg0bcObMmaDoc6f1yy+/xMCBA/H1118jMzMTDzzwAJ5++mlMmjTJpvvzzz+HTCYLWnrp+PHjGDp0KO655x6cOHGCt1i2dQxRqO4fgI37PJxh4fiTnQ2ORrKz3kN21n+YiZiaTCbce++9AIB33nkHFosFFy9exD/+8Q98/vnnWLVqFcaOHWt7s6urq0Nubi7Wr19vu2ilotHKgQMH8O9//xsff/wxNm7cGLS0jbc6f/rpJ3z88cf45JNPsHnzZnTv3l0yGptHPdatW4f58+djz549iIuLC7hGIVo/++wzvP766xg7dqztLfmXX37B66+/jq1bt2LTpk1BOe/nz5/HjBkzUF9fD51Oh4iICLz00kswGAy8kaNQ3D8AG/d5OMPC8Sc7G3yNZGeFQXZWHBQB3bqIcByHS5cuoV+/fra/27Rpg5UrV0KlUuH+++9HamoqevbsCbPZDJVKhYsXL+Kzzz4L2sn2RmNRURG2bt2K33//HVu2bEHXrl2DotFbnYWFhdiyZQvy8/OxZcsWdOvWTXIaremGiRMn4tprr0Xr1q2DotEXrfX19YiIiIBarcYXX3wRtPN++PBhcByHl156CWfPnsXq1asxf/58m9F0TDeF4v4B2LjPwxkWjj/Z2dBoJDvrGbKzIuljJWIKAPfccw+OHTuG7777DjKZzHaj1NfXY9q0aTh//jy+/PJLaDQaSWvcunUrtFotSkpKIJfLodfrJa2zuLgYcrkcsWsWCgcAABIVSURBVLGxktMY6vPtjVbr8QSaSrJEREQEVeMPP/xgS2N++umneO+996BUKrFw4UKkp6eHvAC1FZbOezjCwvEnOxtcjaE+395oJTsrDCmfdyZm5VtPpEajwXfffYfCwkL0798fERERMJlMiIiIgE6nw8aNGzFy5Migv8n5qlGr1UKtVjOhM5gXJwvn2xetN910k01rKEqCpKSk2P6fmZkJtVqNn376Cd999x169+5tm73apUsXm2EPJiyd93CEheNPdja0GkMF2VnxYOG8M5HKt75dXHPNNcjJycHXX38NrVaL++67z3YjWy+Guro6yWusr68PiUZWdLJwvq2wpNWKNZ00fvx4AMDq1avxxBNPQK/XY926dbjllluCOnbMCovHMpxg4fizYL9Y0cnC+bbCklYrZGd9h4mIKdB086pUKuTk5ODYsWPYsWMHfv31V+Tk5KCsrAxr167FiRMn8Ne//jUkbyGsaGRFJwsaWdJqrUkINBkmaxePLl26ICIiAu+88w5OnTqFbdu2BXUcniMsHMtwhoXjz4JGVnSyoJElrWRnxUFyEVPrmJDm4zBMJhOUSiXOnDmDAwcOYOHChVi5ciX+85//oFOnTsjIyEBpaSk+/vjjoLyBsKCRFZ0saGRJqyuNcrkc58+fxzfffIM777wTCoXCts6PP/4IAPjyyy/RpUuXgGsEgOLiYtTW1qJ9+/a2z6R2LMMZlq9lKWlkRScLGlnSSnY2sEiqjunRo0cxffp0FBQU2E622WyGXC7HmTNnMGLECPz4449QKpWYM2cOvv76a7z//vtYunQpvvnmm6DVz5O6RlZ0sqCRJa2eNF5//fX4/fffbetzHIfvv/8eH374ITZt2hQ0Y3nkyBEMGjQIW7duBdA05slq1KVyLMOZcLiWpaCRFZ0saGRJK9nZwCOZWfm//fYbxowZg1GjRmHmzJno2bOnbVlRURFycnJw8803Y+nSpSGb0caCRlZ0sqDRCgta/dFYXFwctDfjQ4cOYdSoUZDL5UhKSsKnn36Ktm3b2nReffXVGD16tCTOezgS7tcy6WRPoxUWtJKdDQ6SGGNqNBoxdepUDB8+HK+88gqSkpJQXV2NoqIiREdHo6GhAZGRkZg/f35IZtmxopEVnSxoZEmrrxqtPZI1Gk1QjNOhQ4cwfPhwzJo1C3PnzsUnn3yCAQMGoGPHjgCA6upq6HQ6PPXUU5I0lqwTztcy6WRTI0tayc4GD0mMMTUajVAoFJg/fz4sFgvuvPNOFBYW4sCBA5g4cSKmTZuG2bNnk8Yw0cmCRissaPVVY/NB+oHm119/xdChQ/HII4/gySefBAB07NgR//znPzFs2DAAQHx8PObMmRNwLS2VcL6WSSebGq2woJXsbPCQxBjTqqoqlJaWoqKiApMnT0ZNTQ0efPBBLF++HCdOnMA///lP/PLLL6QxTHSyoNEKC1qlrtFkMmHjxo148MEH8eSTT8JkMgEAZsyYgdOnT+O7774DYN/bnBAfqV8nrGhkRScLGq2woFXqGsPJzkoiYhoZGYmamhps374dsbGxeOihh5Ceng4AuOqqqzBt2jTs2rUrpINxWdDIik4WNFphQavUNcrlcjz88MPQ6XS2vwFg0KBBMJlM+OqrrzB06FDJppXCBalfJ6xoZEUnCxqtsKBV6hrDyc5KImKakpKCv/zlL3j44Yfx+eefo6amBkCTZ9+rVy/069cPe/fuJY0CYEEnCxqtsKBVyhqtb+dWY2nFbDajbdu2tjFQoY6GtASkfJ2wpBFgQycLGq2woFXKGsPNzgbdMS0sLERlZaXtb+sBnTFjBqZMmYLa2lrs3bsXjY2NdvXB0tLSSCODOlnQyJJWFjQ21+nq7dw67io7OxsajcZW48+afiL8g4XrhAWNrOhkQSNLWlnQ2FxnuNnZoM7KP3bsGLKysvDzzz/j5ptvRkREhO2ARkdHo1OnTrh8+TKWLVuG6upq/Pbbb9i0aRM2b96MhQsXBqXUAgsaWdHJgkaWtLKg0ZVOV7Rt2xb5+flYs2YNZsyYAZVKFRSN4QwL1wkLGlnRyYJGlrSyoNGVTlewZmeDVse0sLAQd911F9RqNfLz85Geno4PPvgAWq3W1lMWAGpqavB///d/WLduHRobGxEXF4enn34a3bt3J40M6WRBI0taWdDoSacjVt1bt27F888/j3Xr1qFNmzZB0RmusHCdsKCRFZ0saGRJKwsaPel0hEU7GzTHdMuWLfj0009x3333QalU4o477kBGRgbef/99p5MONJVmiIqKQl1dXdB6tbKgkRWdLGhkSSsLGoXo5KO+vh5GoxEJCQlB0xmusHCdsKCRFZ0saGRJKwsahejkgyU7GzTH1Gg04qeffsJ1110HAPj5558xdepUpKen4/3330dkZCSAK/1mQwELGlnRyYJGKyxoZUGjNzqb95gmxIOF64QFjazoZEGjFRa0sqDRG52s2tmQtiQ9cOAA7rjjDlx11VU2T3/NmjXIzMxEv379QiXLDhY0AmzoZEGjFRa0sqARYEdnuMLC8WdBI8CGThY0WmFBKwsaAXZ0CiFgjunZs2eRl5eHwsJCDB8+HNHR0byhcOvB7NKlC9q2bYv3338fBw4cQGpqaiBkMaeRFZ0saGRJKwsaWdIZrrBw/FnQyIpOFjSypJUFjSzpFIuAOKaHDx/G+PHjkZiYiNOnTyMyMhITJkzAzJkzkZKS4nQw9+/fj+HDh0Ov1+Ozzz4LSoFaFjSyopMFjSxpZUEjSzrDFRaOPwsaWdHJgkaWtLKgkSWdYiJ6HVOj0YgHHngAkyZNwsaNG3HmzBnccccd2LdvH+bPn4/ff/8dMpnMVhfMZDLhww8/hFarxdatW4NyEFnQyIpOFjSypJUFjSzpDFdYOP4saGRFJwsaWdLKgkaWdIqN6I5pRUUFSkpKMGTIEMTGxgIAnnjiCUydOhVlZWV48cUXUVhYaBuQu3v3buzbtw+bN29GRkaG2HKY1ciKThY0sqSVBY0s6QxXWDj+LGhkRScLGlnSyoJGlnSKjeiOqVwuh0ajwblz5wAAjY2NAIA77rgDt956Kw4fPozt27fb1s/KysKGDRvQq1cvsaUwrZEVnSxoZEkrCxpZ0hmusHD8WdDIik4WNLKklQWNLOkUm4CMMZ08eTLOnDmDjRs3onXr1nalFaZOnYrCwkJ8/fXXIS1lwIJGVnSyoJElrSxoZElnuMLC8WdBIys6WdDIklYWNLKkU0z8jphWVlbCaDSirKzM9tm//vUvVFVVYfr06aiurrar93XdddfBbDajvr4+aAeRBY2s6GRBI0taWdDIks5whYXjz4JGVnSyoJElrSxoZElnoPHLMT169CimTJmC0aNHIzs7G++88w6qq6vRunVrvPXWWzh58iQmTJiAI0eOoKamBkBTOYOoqCjbYN1Aw4JGVnSyoJElrSxoZElnuMLC8WdBIys6WdDIklYWNLKkMxj4nMo/duwYbrrpJkyaNAn9+/fHwYMH8corr2Dz5s0YOHAgACAvLw8zZ85EVVUVYmJi0KZNG+zatQtbt25Ft27dRP0hrGpkRScLGlnSyoJGlnSGKywcfxY0sqKTBY0saWVBI0s6g4VPjmlZWRlmzJiBtLQ0LF682Pb5hAkTkJSUhBUrVtiNd3j77bdx7tw5qNVqjB8/HgaDQbxfwLBGVnSyoJElrSxoZElnuMLC8WdBIys6WdDIklYWNLKkM5gofPlSQ0MDjEYjxo4dC+BK39hOnTqhsLAQAMBxnO3zmTNniqc4jDSyopMFjSxpZUEjSzrDFRaOPwsaWdHJgkaWtLKgkSWdwcSnMaYJCQl48803MWjQIACA2WwGACQlJdkNzJXL5SguLrb9HcxxECxoZEUnCxqtsKCVBY0s6QxXWDj+LGhkRScLGq2woJUFjSzpDCY+T35KS0sD0HQQIyIiAAD19fV2B27JkiVYsmQJ6urqACDos8ZY0MiKThY0WmFBKwsaWdIZrrBw/FnQyIpOFjRaYUErCxpZ0hksfErlN6d5j1YANg//hRdewJIlS/D9999DpVL5uxu/YEEjwIZOFjRaYUErCxoBdnSGKywcfxY0AmzoZEGjFRa0sqARYEdnoJHPnz//GX83YjabwXEc9uzZA4vFghMnTmDJkiX45ptv0LNnTxFk+g8LGgE2dLKg0QoLWlnQCLCjM1xh4fizoBFgQycLGq2woJUFjQA7OgOJ3xFT4IqXL5PJsHbtWkRHR+PLL79EVlaWGJsXBRY0AmzoZEGjFRa0sqARYEdnuMLC8WdBI8CGThY0WmFBKwsaAXZ0BhK/Oz81Z9iwYQCAr776SrK9WlnQCLChkwWNVljQyoJGgB2d4QoLx58FjQAbOlnQaIUFrSxoBNjRGQh8LrDviqqqKuh0OjE3KTosaATY0MmCRissaGVBI8COznCFhePPgkaADZ0saLTCglYWNALs6BQb0R1TgiAIgiAIgvAFUVP5BEEQBEEQBOEr5JgSBEEQBEEQkoAcU4IgCIIgCEISkGNKEARBEARBSAJyTAmCIAiCIAhJQI4pIXnWrl0LvV5v+5eYmIiMjAyMHz8er7/+OioqKnzabl5eHhYuXIjTp0+LrJggCIItyM4SUkGUzk8EEQzmz5+Pjh07oqGhAUVFRdi5cycWLFiAFStW4KOPPkK3bt282t6RI0ewaNEiXH311UhJSQmQaoIgCHYgO0uEGnJMCWa47rrr0LdvX9vfDz/8ML7//ntMmjQJkydPxr59+6DRaEKokCAIgm3IzhKhhlL5BNMMGTIEubm5OHv2LD755BMAwOHDhzFr1ixkZWUhMTERnTt3xowZM/DHH3/Yvrd27VrMmDEDADBmzBhb+mrt2rW2dQ4cOIDbb78dHTp0QFJSEoYNG4Yvv/wyuD+QIAgixJCdJYIJOaYE89x+++0AgO3btwMAduzYgfz8fNx2221YvHgx7rjjDmzbtg1jxoxBTU0NAGDQoEG45557AACPPPII3njjDbzxxhsYNGgQAGDnzp0YMWIEioqKkJubi2effRZKpRKTJ0/Gxo0bQ/ArCYIgQgfZWSJYUEtSQvKsXbsW999/P7Zt22aXYmpOhw4dkJqaiv/+97+orq6GVqu1W757926MHDkSb775Jm677TYAwKeffooZM2Zg06ZNuOaaa2zrWiwW9OvXD0lJSdiwYQNksqb3N7PZjBtvvBGXLl3CL7/8EqBfSxAEEXzIzhJSgSKmRFgQGRmJyspKALAzlpWVlSgtLUV6ejpiYmIEGbpDhw7ZIgFlZWUoKSlBSUkJysrKcP3116OgoABnzpwJ2G8hCIKQImRniWBAk5+IsKCyshJxcXEAAKPRiGeeeQYbNmxAWVmZ3Xrl5eUet3Xy5EkAwJw5czBnzhzedYqLi9GhQwc/VRMEQbAD2VkiGJBjSjDPuXPncPnyZXTq1AkAMH36dOzatQsPPPAAevTogaioKHAch+nTp8NsNnvcnnWdZ555BllZWbzrpKWlifcDCIIgJA7ZWSJYkGNKMM+///1vAMCwYcNgNBqxfft2zJ8/H/Pnz7etU1tbC6PRaPc9juN4t9exY0cATWmroUOHBkY0QRAEQ5CdJYIFjTElmOb777/Hyy+/jJSUFNx22222AfQWi/2cvpUrVzq9xVvHSDka0qysLHTu3BnLly/nTUkVFxeL+RMIgiAkDdlZIphQxJRghm+//RanTp1CY2MjLl26hP/+97/YsWMHkpOT8dFHH0GtVkOtVuPqq6/Ga6+9hoaGBiQnJ2P37t3YtWsXWrVqZbe9nj17QiaT4ZVXXkF5eTk0Gg369OmD1NRU/Otf/8KECRMwYMAATJkyBR06dMDFixexf/9+nD17Fnv27AnRUSAIgggcZGeJUEOOKcEML730EgBAqVQiNjYWmZmZWLhwIaZMmYKoqCjbem+//Tbmz5+P1atXo7GxETk5Odi4cSPGjh1rt702bdrg1Vdfxauvvoq5c+fCZDJhxYoVSE1NxcCBA/Htt99i8eLFeO+993D58mXEx8ejW7duWLBgQVB/N0EQRLAgO0uEGqpjShAEQRAEQUgCGmNKEARBEARBSAJyTAmCIAiCIAhJQI4pQRAEQRD/324dCwAAAAAM8reexo6iCBbEFACABTEFAGBBTAEAWBBTAAAWxBQAgAUxBQBgQUwBAFgIeFyDkKd81SgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 准备画图\n",
    "# 指定默认风格\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# 设置布局\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2,\n",
    "                                             ncols=2,\n",
    "                                             figsize=(10, 10))\n",
    "fig.autofmt_xdate(rotation=45)\n",
    "\n",
    "# 标签值\n",
    "ax1.plot(dates, features['actual']) # 所用数据列\n",
    "ax1.set_xlabel('') # x轴数据\n",
    "ax1.set_ylabel('Temperature') # y轴数据\n",
    "ax1.set_title('Max Temp') # 图表标题\n",
    "\n",
    "# 昨天\n",
    "ax2.plot(dates, features['temp_1'])\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Temperature')\n",
    "ax2.set_title('Previous Max Temp')\n",
    "\n",
    "# 前天\n",
    "ax3.plot(dates, features['temp_2'])\n",
    "ax3.set_xlabel('Date')\n",
    "ax3.set_ylabel('Temperature')\n",
    "ax3.set_title('Two Days Prior Max Temp')\n",
    "\n",
    "# 朋友预测值（意义不大）\n",
    "ax4.plot(dates, features['friend'])\n",
    "ax4.set_xlabel('Date')\n",
    "ax4.set_ylabel('Temperature')\n",
    "ax4.set_title('Friend Estimate')\n",
    "\n",
    "plt.tight_layout(pad=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>temp_2</th>\n",
       "      <th>temp_1</th>\n",
       "      <th>average</th>\n",
       "      <th>actual</th>\n",
       "      <th>friend</th>\n",
       "      <th>week_Fri</th>\n",
       "      <th>week_Mon</th>\n",
       "      <th>week_Sat</th>\n",
       "      <th>week_Sun</th>\n",
       "      <th>week_Thurs</th>\n",
       "      <th>week_Tues</th>\n",
       "      <th>week_Wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45.6</td>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>45.7</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>45.8</td>\n",
       "      <td>41</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>45.9</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>46.0</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n",
       "0  2016      1    1      45      45     45.6      45      29         1   \n",
       "1  2016      1    2      44      45     45.7      44      61         0   \n",
       "2  2016      1    3      45      44     45.8      41      56         0   \n",
       "3  2016      1    4      44      41     45.9      40      53         0   \n",
       "4  2016      1    5      41      40     46.0      44      41         0   \n",
       "\n",
       "   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
       "0         0         0         0           0          0         0  \n",
       "1         0         1         0           0          0         0  \n",
       "2         0         0         1           0          0         0  \n",
       "3         1         0         0           0          0         0  \n",
       "4         0         0         0           0          1         0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 独热编码（字符串数据转为数值形式）\n",
    "features = pd.get_dummies(features)\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# 标签\n",
    "labels = np.array(features['actual'])\n",
    "\n",
    "# 在特征中去掉标签\n",
    "# axis = 1 表示沿着每一行或者列标签横向执行对应的方法\n",
    "# https://blog.csdn.net/wyf2017/article/details/107459509\n",
    "features = features.drop('actual', axis=1)\n",
    "\n",
    "# 每列的名字单独保存一下，以备后患\n",
    "feature_list = list(features.columns)\n",
    "\n",
    "# 转换成合适的格式\n",
    "# np.array()把列表转化为数组\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45, 44, 41, 40, 44, 51, 45, 48, 50, 52, 45, 49, 55, 49, 48, 54, 50,\n",
       "       54, 48, 52, 52, 57, 48, 51, 54, 56, 57, 56, 52, 48, 47, 46, 51, 49,\n",
       "       49, 53, 49, 51, 57, 62, 56, 55, 58, 55, 56, 57, 53, 51, 53, 51, 51,\n",
       "       60, 59, 61, 60, 57, 53, 58, 55, 59, 57, 64, 60, 53, 54, 55, 56, 55,\n",
       "       52, 54, 49, 51, 53, 58, 63, 61, 55, 56, 57, 53, 54, 57, 59, 51, 56,\n",
       "       64, 68, 73, 71, 63, 69, 60, 57, 68, 77, 76, 66, 59, 58, 60, 59, 59,\n",
       "       60, 68, 77, 89, 81, 81, 73, 64, 65, 55, 59, 60, 61, 64, 61, 68, 77,\n",
       "       87, 74, 60, 68, 77, 82, 63, 67, 75, 81, 77, 82, 65, 57, 60, 71, 64,\n",
       "       63, 66, 59, 66, 65, 66, 66, 65, 64, 64, 64, 71, 79, 75, 71, 80, 81,\n",
       "       92, 86, 85, 67, 65, 67, 65, 70, 66, 60, 67, 71, 67, 65, 70, 76, 73,\n",
       "       75, 68, 69, 71, 78, 85, 79, 74, 73, 76, 76, 71, 68, 69, 76, 68, 74,\n",
       "       71, 74, 74, 77, 75, 77, 76, 72, 80, 73, 78, 82, 81, 71, 75, 80, 85,\n",
       "       79, 83, 85, 88, 76, 73, 77, 73, 75, 80, 79, 72, 72, 73, 72, 76, 80,\n",
       "       87, 90, 83, 84, 81, 79, 75, 70, 67, 68, 68, 68, 67, 72, 74, 77, 70,\n",
       "       74, 75, 79, 71, 75, 68, 69, 71, 67, 68, 67, 64, 67, 76, 77, 69, 68,\n",
       "       66, 67, 63, 65, 61, 63, 66, 63, 64, 68, 57, 60, 62, 66, 60, 60, 62,\n",
       "       60, 60, 61, 58, 62, 59, 62, 62, 61, 65, 58, 60, 65, 68, 59, 57, 57,\n",
       "       65, 65, 58, 61, 63, 71, 65, 64, 63, 59, 55, 57, 55, 50, 52, 55, 57,\n",
       "       55, 54, 54, 49, 52, 52, 53, 48, 52, 52, 52, 46, 50, 49, 46, 40, 42,\n",
       "       40, 41, 36, 44, 44, 43, 40, 39, 39, 35, 35, 39, 46, 51, 49, 45, 40,\n",
       "       41, 42, 42, 47, 48, 48, 57, 40], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['year',\n",
       " 'month',\n",
       " 'day',\n",
       " 'temp_2',\n",
       " 'temp_1',\n",
       " 'average',\n",
       " 'friend',\n",
       " 'week_Fri',\n",
       " 'week_Mon',\n",
       " 'week_Sat',\n",
       " 'week_Sun',\n",
       " 'week_Thurs',\n",
       " 'week_Tues',\n",
       " 'week_Wed']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.016e+03, 1.000e+00, 1.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [2.016e+03, 1.000e+00, 2.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [2.016e+03, 1.000e+00, 3.000e+00, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       ...,\n",
       "       [2.016e+03, 1.200e+01, 2.900e+01, ..., 1.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [2.016e+03, 1.200e+01, 3.000e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00],\n",
       "       [2.016e+03, 1.200e+01, 3.100e+01, ..., 0.000e+00, 0.000e+00,\n",
       "        0.000e+00]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(348, 14)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# 不仅计算训练数据的均值和方差，还会基于计算出来的均值和方差来转换训练数据，从而把数据转换成标准的正太分布\n",
    "input_features = preprocessing.StandardScaler().fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.016e+03, 1.000e+00, 1.000e+00, 4.500e+01, 4.500e+01, 4.560e+01,\n",
       "       2.900e+01, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "       0.000e+00, 0.000e+00])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -1.5678393 , -1.65682171, -1.48452388, -1.49443549,\n",
       "       -1.3470703 , -1.98891668,  2.44131112, -0.40482045, -0.40961596,\n",
       "       -0.40482045, -0.40482045, -0.41913682, -0.40482045])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(input_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-01T13:59:02.856817Z",
     "start_time": "2022-08-01T13:59:01.621121Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: tensor(4378.8550, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(156.1447, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(147.6418, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(144.8679, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(143.4154, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(142.5159, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(141.8821, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(141.4163, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(141.0639, dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
      "loss: tensor(140.7854, dtype=torch.float64, grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor(input_features, dtype=float)\n",
    "\n",
    "y = torch.tensor(labels, dtype=float)\n",
    "\n",
    "# 权重参数初始化，构建2层神经网络 （只有1个隐含层）\n",
    "# [348,14]->[14,128]->[128,1]（输入层->隐含层->输出层）\n",
    "# 第1层有128个神经元\n",
    "# randn，从标准正态分布中返回⼀个或多个样本值\n",
    "weights = torch.randn((14, 128), dtype=float, requires_grad=True) #权值矩阵w\n",
    "biases = torch.randn(128, dtype=float, requires_grad=True) # 偏置向量b\n",
    "# 第2层有1个神经元\n",
    "weights2 = torch.randn((128, 1), dtype=float, requires_grad=True)\n",
    "biases2 = torch.randn(1, dtype=float, requires_grad=True)\n",
    "\n",
    "learning_rate = 0.001\n",
    "losses = []\n",
    "\n",
    "for i in range(1000):\n",
    "    # 计算隐层\n",
    "    hidden = x.mm(weights) + biases\n",
    "    # 加入激活函数\n",
    "    hidden = torch.relu(hidden)\n",
    "    # 预测结果\n",
    "    predictions = hidden.mm(weights2) + biases2\n",
    "    # 通计算损失\n",
    "    loss = torch.mean((predictions - y)**2)\n",
    "    losses.append(loss.data.numpy())\n",
    "\n",
    "    # 打印损失值\n",
    "    if i % 100 == 0:\n",
    "        print('loss:', loss)\n",
    "    #返向传播计算\n",
    "    loss.backward()\n",
    "\n",
    "    #更新参数\n",
    "    weights.data.add_(-learning_rate * weights.grad.data)\n",
    "    biases.data.add_(-learning_rate * biases.grad.data)\n",
    "    weights2.data.add_(-learning_rate * weights2.grad.data)\n",
    "    biases2.data.add_(-learning_rate * biases2.grad.data)\n",
    "\n",
    "    # 每次迭代都得记得清空\n",
    "    weights.grad.data.zero_()\n",
    "    biases.grad.data.zero_()\n",
    "    weights2.grad.data.zero_()\n",
    "    biases2.grad.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([348, 1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更简单的构建网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = input_features.shape[1] #样本个数\n",
    "hidden_size = 128 # 隐含层神经元个数\n",
    "output_size = 1\n",
    "batch_size = 16\n",
    "my_nn = torch.nn.Sequential(\n",
    "    torch.nn.Linear(input_size, hidden_size), #全连接层\n",
    "    torch.nn.Sigmoid(), #激活函数\n",
    "    torch.nn.Linear(hidden_size, output_size),\n",
    ")\n",
    "# MSE损失函数\n",
    "cost = torch.nn.MSELoss(reduction='mean')\n",
    "\n",
    "# Adam优化器\n",
    "optimizer = torch.optim.Adam(my_nn.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-01T13:58:03.767891Z",
     "start_time": "2022-08-01T13:57:51.567342Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 34.38214\n",
      "100 34.252792\n",
      "200 34.120964\n",
      "300 33.988102\n",
      "400 33.85364\n",
      "500 33.716076\n",
      "600 33.573933\n",
      "700 33.426205\n",
      "800 33.27164\n",
      "900 33.11107\n"
     ]
    }
   ],
   "source": [
    "# 训练网络\n",
    "losses = []\n",
    "for i in range(1000):\n",
    "    batch_loss = []\n",
    "    # MINI-Batch方法来进行训练\n",
    "    for start in range(0, len(input_features), batch_size):\n",
    "        end = start + batch_size if start + batch_size < len(\n",
    "            input_features) else len(input_features)\n",
    "        xx = torch.tensor(input_features[start:end],\n",
    "                          dtype=torch.float,\n",
    "                          requires_grad=True)\n",
    "        yy = torch.tensor(labels[start:end],\n",
    "                          dtype=torch.float,\n",
    "                          requires_grad=True)\n",
    "        prediction = my_nn(xx)# 前向传播\n",
    "        loss = cost(prediction, yy) # 计算损失\n",
    "        optimizer.zero_grad() # 梯度清零\n",
    "        loss.backward(retain_graph=True) #反向传播\n",
    "        optimizer.step() # 更新参数\n",
    "        batch_loss.append(loss.data.numpy()) #记录损失，便于打印\n",
    "\n",
    "    # 打印损失\n",
    "    if i % 100 == 0:\n",
    "        losses.append(np.mean(batch_loss))\n",
    "        print(i, np.mean(batch_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor(input_features, dtype=torch.float)\n",
    "\n",
    "# 转换成numpy格式便于画图\n",
    "predict = my_nn(x).data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "       [48.458603]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 转换日期格式\n",
    "dates = [\n",
    "    str(int(year)) + '-' + str(int(month)) + '-' + str(int(day))\n",
    "    for year, month, day in zip(years, months, days)\n",
    "]\n",
    "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n",
    "\n",
    "# 创建一个表格来存日期和其对应的标签数值\n",
    "true_data = pd.DataFrame(data={'date': dates, 'actual': labels})\n",
    "\n",
    "# 同理，再创建一个来存日期和其对应的模型预测值\n",
    "months = features[:, feature_list.index('month')]\n",
    "days = features[:, feature_list.index('day')]\n",
    "years = features[:, feature_list.index('year')]\n",
    "\n",
    "test_dates = [\n",
    "    str(int(year)) + '-' + str(int(month)) + '-' + str(int(day))\n",
    "    for year, month, day in zip(years, months, days)\n",
    "]\n",
    "\n",
    "test_dates = [\n",
    "    datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates\n",
    "]\n",
    "\n",
    "predictions_data = pd.DataFrame(data={\n",
    "    'date': test_dates,\n",
    "    'prediction': predict.reshape(-1)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-01T14:05:12.213118Z",
     "start_time": "2022-08-01T14:05:12.011257Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual and Predicted Values')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "# 真实值\n",
    "plt.plot(true_data['date'], true_data['actual'], 'b-', label='actual')\n",
    "\n",
    "# 预测值\n",
    "plt.plot(predictions_data['date'],\n",
    "         predictions_data['prediction'],\n",
    "         'ro',\n",
    "         label='prediction')\n",
    "plt.xticks(rotation='60')\n",
    "plt.legend()\n",
    "\n",
    "# 图名\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Maximum Temperature (F)')\n",
    "plt.title('Actual and Predicted Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
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